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arxiv: 2606.29329 · v1 · pith:VE7LYPJSnew · submitted 2026-06-28 · 💻 cs.CV

RAGA: Real Time Ray Traced Gaussian Shadow Casting for 3DGS Avatar-Scene Interaction

Aymen Mir , Riza Alp Guler , Jian Wang , Peter Wonka , Bing Zhou , Gerard Pons-Moll This is my paper

Pith reviewed 2026-06-30 07:36 UTC · model grok-4.3

classification 💻 cs.CV
keywords 3D Gaussian Splattingshadow castingray tracingreal-time renderingavatar animationGaussian space
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The pith

Shadow computation for 3D Gaussian Splatting avatars occurs entirely in Gaussian space via ray-Gaussian line integrals without mesh reconstruction.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces RAGA for producing physically plausible shadows when animating 3DGS avatars inside existing scenes, either alone or with other avatars and objects. Instead of relying on binary hit tests or converting the Gaussians to meshes, the method integrates the opacity profile of each occluding Gaussian along the shadow ray and normalizes the result by the theoretical maximum integral. This yields a continuous weight describing how fully the ray is blocked. A separate avatar proxy representation is added to dampen temporal variance caused by clothing motion during animation. The full pipeline is implemented with custom CUDA kernels inside the OptiX ray-tracing framework and runs at interactive rates.

Core claim

RAGA performs shadow computation entirely in Gaussian space without requiring any mesh reconstruction. For each occluding Gaussian, the opacity profile along the shadow ray is integrated and normalized by the theoretical maximum integral, producing a weight that captures how the ray traverses the occluder. An avatar proxy representation stabilizes the resulting shadows for animated avatars while preserving visual fidelity.

What carries the argument

Ray-Traced Gaussian Shadow Casting formulation that computes exact ray-Gaussian line integrals of opacity profiles and normalizes them to obtain traversal weights.

If this is right

  • Avatar shadows can be generated inside 3DGS scenes without ever building a mesh.
  • The same formulation supports single-avatar, multi-avatar, and avatar-object interaction cases.
  • Custom CUDA kernels integrated with OptiX deliver roughly 50 FPS shadow tracing.
  • The avatar proxy reduces temporal variance from clothing deformations while keeping visual quality.
  • Shadow realism and scene coherence improve relative to prior binary or mesh-dependent approaches.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The line-integral approach might be reused for other light-transport effects such as soft reflections inside Gaussian scenes.
  • Proxy stabilization could be adapted to other time-varying phenomena in 3DGS beyond shadows.
  • Avoiding meshes opens the possibility of mixing 3DGS avatars with purely implicit or point-cloud scene elements.

Load-bearing premise

Integrating and normalizing opacity profiles along shadow rays produces accurate, temporally stable shadow weights for animated avatars without mesh-based methods or post-processing.

What would settle it

Side-by-side comparison of shadow boundaries and temporal stability between RAGA output and a mesh-based ray tracer on the same animated avatar sequence, looking for visible mismatches or flickering.

Figures

Figures reproduced from arXiv: 2606.29329 by Aymen Mir, Bing Zhou, Gerard Pons-Moll, Jian Wang, Peter Wonka, Riza Alp Guler.

Figure 1
Figure 1. Figure 1: RAGA casts physically plausible shadows for animated 3DGS avatars [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Method overview. Left: a shadow ray is cast from a scene Gaussian toward the light source through an animated 3DGS avatar by accumulating ray transmittance over all Gaussians in the avatar. Right: comparison of ray–Gaussian intersection strategies. (a) The icosahedron proxy used by 3DGRT [79] produces incorrect hits outside the Gaussian support. (b) Our exact quadratic test (Sec. 3.2) finds precise entry/e… view at source ↗
Figure 3
Figure 3. Figure 3: Avatar proxy. We replace the pose￾dependent anisotropic Gaussians (left) with a stable isotropic proxy (right) that elimi￾nates temporal flickering in shadows. While the pose-dependent 3DGS avatar model produces high-fidelity renderings, using the raw primitives for shadow casting introduces tempo￾ral instability. The pose-to-geometry generator H(θt), a neural network that maps body pose θt to 3D Gaus￾sian… view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative comparison against baselines on SuperSplat [101] scenes. Pseudo￾GT is obtained using a proxy floor mesh and posed SMPL mesh via classical shadow mapping. Our method produces soft, physically plausible shadows that closely match the pseudo-GT. Both 3DGRT [79] and RaySplat [10] lose shadow detail due to their implausible attenuation models. – ScanNet++ [120] scenes: Since ScanNet++ provides both … view at source ↗
Figure 5
Figure 5. Figure 5: Visual ablations. Without Imax normalization (left), large anisotropic Gaus￾sians cast disproportionately dark shadows. The linear attenuation model (middle) produces harder shadow falloff than our full exponentiated model (right). 4.3 Temporal Stability A key contribution of RAGA is the avatar proxy (Sec. 3.8) that ensures temporally stable shadows under animation. We measure temporal shadow consistency (… view at source ↗
Figure 6
Figure 6. Figure 6: Runtime vs. avatar Gaussians. No proxy Ours (proxy) [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: Qualitative results: our method casts plausible soft shadows for various 3DGS avatars animated alone and with objects across diverse scenes. integral and analytically computable entry and exit points, our method enables stable and physically grounded aggregation of shadow contributions from multiple volumetric occluders. This principled formulation avoids the approximations commonly used in existing ray tr… view at source ↗
read the original abstract

We study the problem of physically plausible shadow casting when animating 3D Gaussian Splatting (3DGS) avatars, either individually or in multi-avatar and object-interaction scenarios, within existing 3DGS scenes. In contrast to prior methods that rely on binary hit tests and mesh-based shadow casters, our method performs shadow computation entirely in Gaussian space, without requiring any mesh reconstruction. We introduce RAGA, a Ray-Traced Gaussian Shadow Casting formulation based on exact ray-Gaussian line integrals. For each occluding Gaussian, we integrate the opacity profile along the shadow ray and normalize by the theoretical maximum integral, producing a weight that captures how the ray traverses the occluder rather than merely whether an intersection occurred. To reduce temporal variance from clothing deformations in animated avatars, we further introduce an avatar proxy representation that stabilizes shadow casting while preserving visual fidelity. We implement RAGA using custom CUDA kernels integrated with the NVIDIA OptiX framework; as such, our shadow tracer runs at rates of about 50 FPS. We evaluate on single-avatar, multi-avatar, and avatar-object interaction scenarios across multiple datasets, demonstrating substantially improved shadow realism, temporal stability, and scene coherence. Our project page is available at https://miraymen.github.io/raga/.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 3 minor

Summary. The manuscript introduces RAGA, a method for computing physically plausible shadows when animating 3D Gaussian Splatting (3DGS) avatars in existing scenes. It performs all shadow computation in Gaussian space via exact ray-Gaussian line integrals of opacity along each shadow ray; each integral is normalized by its theoretical maximum to produce a continuous traversal weight rather than a binary hit. An avatar proxy representation is added to reduce temporal variance from clothing deformations. The approach is implemented with custom CUDA kernels inside the NVIDIA OptiX framework and runs at approximately 50 FPS. Evaluations on single-avatar, multi-avatar, and avatar-object interaction scenarios across multiple datasets are reported to show improved shadow realism, temporal stability, and scene coherence without any mesh reconstruction.

Significance. If the formulation and results hold, the work supplies a mesh-free, closed-form solution to a practical rendering problem in dynamic 3DGS avatars. The direct use of line integrals over anisotropic Gaussians and the proxy stabilization mechanism address known animation artifacts while preserving real-time performance, which could facilitate more coherent avatar-scene interactions in existing Gaussian pipelines.

minor comments (3)
  1. [Abstract] Abstract: the phrase 'substantially improved shadow realism' is stated without any numerical comparison or metric; adding one or two quantitative highlights would make the summary more informative.
  2. The description of the avatar proxy (how it is constructed and how its parameters are chosen) would benefit from an explicit equation or pseudocode block to clarify its relation to the underlying Gaussians.
  3. Implementation section: the reported 50 FPS figure should specify the test resolution, number of Gaussians, and hardware to allow direct reproducibility.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive summary of our manuscript, the favorable significance assessment, and the recommendation for minor revision. No specific major comments appear in the report.

Circularity Check

0 steps flagged

No significant circularity; derivation is self-contained from ray integration

full rationale

The central formulation computes shadow weights via exact line integrals of Gaussian opacity along rays, normalized by the theoretical maximum integral value. This is a closed-form reduction of the quadratic form for anisotropic Gaussians, directly derived from ray-tracing principles without fitted parameters, self-citation load-bearing premises, or renaming of empirical patterns. The avatar proxy is introduced as an explicit stabilization heuristic for animation artifacts, not as a hidden fit. No load-bearing step reduces to its own inputs by construction. The method is externally falsifiable via visual comparison on interaction scenarios.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review based on abstract only; no specific free parameters, axioms, or invented entities identifiable. Full text would be needed to audit these.

pith-pipeline@v0.9.1-grok · 5775 in / 1207 out tokens · 35261 ms · 2026-06-30T07:36:44.658040+00:00 · methodology

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Reference graph

Works this paper leans on

134 extracted references · 7 canonical work pages

  1. [1]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)

    Abdal, R., Yifan, W., Shi, Z., Xu, Y., Po, R., Kuang, Z., Chen, Q., Yeung, D.Y., Wetzstein, G.: Gaussian shell maps for efficient 3d human generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)

    2024

  2. [2]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)

    Alldieck, T., Magnor, M., Bhatnagar, B.L., Theobalt, C., Pons-Moll, G.: Learning to reconstruct people in clothing from a single RGB camera. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)

    2019

  3. [3]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2018)

    Alldieck, T., Magnor, M., Xu, W., Theobalt, C., Pons-Moll, G.: Video based reconstruction of 3d people models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2018)

    2018

  4. [4]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2021)

    Alldieck, T., Xu, H., Sminchisescu, C.: imGHUM: Implicit generative models of 3d human shape and articulated pose. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2021)

    2021

  5. [5]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)

    Barron,J.T.,Mildenhall,B.,Verbin,D.,Srinivasan,P.P.,Hedman,P.:Mip-nerf360: Unbounded anti-aliased neural radiance fields. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)

    2022

  6. [6]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2023)

    Barron, J.T., Mildenhall, B., Verbin, D., Srinivasan, P.P., Hedman, P.: Zip-nerf: Anti-aliased grid-based neural radiance fields. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2023)

    2023

  7. [7]

    In: ACM SIGGRAPH Asia Conference Papers (2024)

    Bi, Z., Zeng, Y., Zeng, C., Pei, F., Feng, X., Zhou, K., Wu, H.: Gs3: Efficient relighting with triple gaussian splatting. In: ACM SIGGRAPH Asia Conference Papers (2024)

    2024

  8. [8]

    In: European Conference on Computer Vision (ECCV) (2016)

    Bogo, F., Kanazawa, A., Lassner, C., Gehler, P., Romero, J., Black, M.J.: Keep it SMPL: Automatic estimation of 3D human pose and shape from a single image. In: European Conference on Computer Vision (ECCV) (2016)

    2016

  9. [9]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)

    Bolanos, L., Su, S.Y., Rhodin, H.: Gaussian shadow casting for neural characters. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)

    2024

  10. [10]

    InProceedings of the IEEE conference on computer vision and pattern recognition

    Byrski, K., Mazur, M., Tabor, J., Dziarmaga, T., Kądziołka, M., Baran, D., Spurek, P.: Raysplats: Ray tracing based gaussian splatting. arXiv preprint arXiv:2501.19196 (2025)

    work page arXiv 2025

  11. [11]

    In: International Conference on Learning Representations (ICLR) (2025)

    Chen, H., Lin, Z., Zhang, J.: GI-GS: Global illumination decomposition on gaus- sian splatting for inverse rendering. In: International Conference on Learning Representations (ICLR) (2025)

    2025

  12. [12]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2021)

    Chen, X., Zheng, Y., Black, M.J., Hilliges, O., Geiger, A.: Snarf: Differentiable forward skinning for animating non-rigid neural implicit shapes. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2021)

    2021

  13. [13]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)

    Chen, Y., Chen, Z., Zhang, C., Wang, F., Yang, X., Wang, Y., Cai, Z., Yang, L., Liu, H., Lin, G.: Gaussianeditor: Swift and controllable 3d editing with gaussian splatting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)

    2024

  14. [14]

    Chen,Z.,Liu,Z.:Relighting4d:Neuralrelightablehumanfromvideos.In:European Conference on Computer Vision (ECCV) (2022)

    2022

  15. [15]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024) RAGA: Ray Traced Gaussian Shadow Casting 17

    Chen, Z., et al.: Urhand: Universal relightable hands. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024) RAGA: Ray Traced Gaussian Shadow Casting 17

    2024

  16. [16]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2023)

    Cheng, W., Chen, R., Yin, W., Fan, S., Chen, K., He, H., Luo, H., Cai, Z., Wang, J., Gao, Y., Yu, Z., Lin, Z., Ren, D., Yang, L., Liu, Z., Loy, C.C., Qian, C., Wu, W., Lin, D., Dai, B., Lin, K.Y.: Dna-rendering: A diverse neural actor repository for high- fidelity human-centric rendering. In: Proceedings of the IEEE/CVF International Conference on Compute...

    2023

  17. [17]

    In: European Conference on Computer Vision (ECCV) (2020)

    Deng, B., Lewis, J.P., Jeruzalski, T., Pons-Moll, G., Hinton, G., Norouzi, M., Tagliasacchi, A.: Nasa neural articulated shape approximation. In: European Conference on Computer Vision (ECCV) (2020)

    2020

  18. [18]

    In: Advances in Neural Information Processing Systems (NeurIPS) (2024)

    Dihlmann, J.N., Majumdar, A., Engelhardt, A., Braun, R., Lensch, H.P.: Sub- surface scattering for 3D gaussian splatting. In: Advances in Neural Information Processing Systems (NeurIPS) (2024)

    2024

  19. [19]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)

    Diller, C., Dai, A.: Cg-hoi: Contact-guided 3d human-object interaction generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)

    2024

  20. [20]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2025)

    Du, K., Liang, Z., Shen, Y., Wang, Z.: GS-ID: Illumination decomposition on gaussian splatting via adaptive light aggregation and diffusion-guided material priors. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2025)

    2025

  21. [21]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2025)

    Fan, J., Luan, F., Yang, J., Hasan, M., Wang, B.: Rng: Relightable neural gaussians. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2025)

    2025

  22. [22]

    International journal of computer vision110(3) (2014)

    Fouhey, D.F., Delaitre, V., Gupta, A., Efros, A.A., Laptev, I., Sivic, J.: People watching: Human actions as a cue for single view geometry. International journal of computer vision110(3) (2014)

    2014

  23. [23]

    In: European Conference on Computer Vision (ECCV) (2024)

    Gao, J., Gu, C., Lin, Y., Li, Z., Zhu, H., Cao, X., Zhang, L., Yao, Y.: Relightable 3d gaussians: Realistic point cloud relighting with brdf decomposition and ray tracing. In: European Conference on Computer Vision (ECCV) (2024)

    2024

  24. [24]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2025)

    Gu, C., Wei, X., Zeng, Z., Yao, Y., Zhang, L.: IRGS: Inter-reflective gaussian splat- ting with 2D gaussian ray tracing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2025)

    2025

  25. [25]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)

    Guédon, A., Lepetit, V.: Sugar: Surface-aligned gaussian splatting for efficient 3d mesh reconstruction and high-quality mesh rendering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)

    2024

  26. [26]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)

    Guo, C., Jiang, T., Chen, X., Song, J., Hilliges, O.: Vid2avatar: 3d avatar re- construction from videos in the wild via self-supervised scene decomposition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)

    2023

  27. [27]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)

    Guo, C., Zou, S., Zuo, X., Wang, S., Ji, W., Li, X., Cheng, L.: Generating diverse and natural 3d human motions from text. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)

    2022

  28. [28]

    ACM Transactions on Graphics (2019)

    Guo, K., Lincoln, P., Davidson, P., Busch, J., Yu, X., Whalen, M., Harvey, G., Orts Escolano, S., Pandey, R., Dourgarian, J., Tang, D., Tkach, A., Kowdle, A., Cooper, E., Dou, M., Fanello, S., Fyffe, G., Rhemann, C., Taylor, J., Debevec, P., Izadi, S.: The relightables: Volumetric performance capture of humans with realistic relighting. ACM Transactions o...

    2019

  29. [29]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2011) 18 A

    Gupta, A., Satkin, S., Efros, A.A., Hebert, M.: From 3d scene geometry to human workspace. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2011) 18 A. Mir et al

    2011

  30. [30]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)

    Guzov, V., Mir, A., Sattler, T., Pons-Moll, G.: Human poseitioning system (hps): 3d human pose estimation and self-localization in large scenes from body-mounted sensors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)

    2021

  31. [31]

    Proceedings of the ACM on Computer Graphics and Interactive Techniques6(3) (2023)

    Habermann, M., Liu, L., Xu, W., Pons-Moll, G., Zollhoefer, M., Theobalt, C.: Hdhumans: A hybrid approach for high-fidelity digital humans. Proceedings of the ACM on Computer Graphics and Interactive Techniques6(3) (2023)

    2023

  32. [32]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2021)

    Hassan, M., Ceylan, D., Villegas, R., Saito, J., Yang, J., Zhou, Y., Black, M.: Stochastic Scene-Aware motion prediction. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2021)

    2021

  33. [33]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2019)

    Hassan, M., Choutas, V., Tzionas, D., Black, M.J.: Resolving 3D human pose ambi- guities with 3D scene constraints. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2019)

    2019

  34. [34]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)

    Hassan, M., Ghosh, P., Tesch, J., Tzionas, D., Black, M.J.: Populating 3D scenes by learning human-scene interaction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)

    2021

  35. [35]

    In: Advances in Neural Information Processing Systems (NeurIPS) (2022)

    Hasselgren, J., Hofmann, N., Munkberg, J.: Shape, light, and material decompo- sition from images using monte carlo rendering and denoising. In: Advances in Neural Information Processing Systems (NeurIPS) (2022)

    2022

  36. [36]

    In: ACM SIGGRAPH Asia Conference Papers (2024)

    He, M., Clausen, P., Taşel, A.L., Ma, L., Pilarski, O., Xian, W., Rikker, L., Yu, X., Burgert, R., Yu, N., Debevec, P.: Diffrelight: Diffusion-based facial performance relighting. In: ACM SIGGRAPH Asia Conference Papers (2024)

    2024

  37. [37]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2021)

    He, T., Xu, Y., Saito, S., Soatto, S., Tung, T.: Arch++: Animation-ready clothed human reconstruction revisited. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2021)

    2021

  38. [38]

    In: Advances in Neural Information Processing Systems (NeurIPS) (2020)

    Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Advances in Neural Information Processing Systems (NeurIPS) (2020)

    2020

  39. [39]

    In: ACM SIGGRAPH Conference Proceedings (2024)

    Huang, B., Yu, Z., Chen, A., Geiger, A., Gao, S.: 2d gaussian splatting for geo- metrically accurate radiance fields. In: ACM SIGGRAPH Conference Proceedings (2024)

    2024

  40. [40]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)

    Huang, Z., Xu, Y., Lassner, C., Li, H., Tung, T.: Arch: Animatable reconstruction of clothed humans. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)

    2020

  41. [41]

    ACM Transactions on Graphics42(4) (2023)

    Işık, M., Rünz, M., Georgopoulos, M., Khakhulin, T., Starck, J., Agapito, L., Nießner, M.: Humanrf: High-fidelity neural radiance fields for humans in motion. ACM Transactions on Graphics42(4) (2023)

    2023

  42. [42]

    In: Proceedings of the IEEE/CVF Interna- tional Conference on Computer Vision (ICCV) (2023)

    Iqbal, U., Caliskan, A., Nagano, K., Khamis, S., Molchanov, P., Kautz, J.: Rana: Relightable articulated neural avatars. In: Proceedings of the IEEE/CVF Interna- tional Conference on Computer Vision (ICCV) (2023)

    2023

  43. [43]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)

    Iwase, S., Saito, S., Simon, T., Lombardi, S., Bagautdinov, T., Joshi, R., Prada, F., Shiratori, T., Sheikh, Y., Saragih, J.: Relightablehands: Efficient neural relighting of articulated hand models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)

    2023

  44. [44]

    In: European Conference on Computer Vision (ECCV) (2022)

    Ji, C., Yu, T., Guo, K., Liu, J., Liu, Y.: Geometry-aware single-image full-body human relighting. In: European Conference on Computer Vision (ECCV) (2022)

    2022

  45. [45]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024) RAGA: Ray Traced Gaussian Shadow Casting 19

    Jiang, N., Zhang, Z., Li, H., Ma, X., Wang, Z., Chen, Y., Liu, T., Zhu, Y., Huang, S.: Scaling up dynamic human-scene interaction modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024) RAGA: Ray Traced Gaussian Shadow Casting 19

    2024

  46. [46]

    In: European Conference on Computer Vision (ECCV) (2022)

    Jiang, W., Yi, K.M., Samei, G., Tuzel, O., Ranjan, A.: Neuman: Neural human radiance field from a single video. In: European Conference on Computer Vision (ECCV) (2022)

    2022

  47. [47]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)

    Jiang,Y.,Tu,J.,Liu,Y.,Gao,X.,Long,X.,Wang,W.,Ma,Y.:GaussianShader:3D gaussian splatting with shading functions for reflective surfaces. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)

    2024

  48. [48]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)

    Jin, H., Liu, I., Xu, P., Zhang, X., Han, S., Bi, S., Zhou, X., Xu, Z., Su, H.: Tensoir: Tensorial inverse rendering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)

    2023

  49. [49]

    In: ACM SIGGRAPH Conference Proceedings (2025)

    Junkawitsch, H., Sun, G., Zhu, H., Theobalt, C., Habermann, M.: Eva: Expres- sive virtual avatars from multi-view videos. In: ACM SIGGRAPH Conference Proceedings (2025)

    2025

  50. [50]

    ACM Transactions on Graphics37(6) (2018)

    Kanamori, Y., Endo, Y.: Relighting humans: Occlusion-aware inverse rendering for full-body human images. ACM Transactions on Graphics37(6) (2018)

    2018

  51. [51]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2018)

    Kanazawa, A., Black, M.J., Jacobs, D.W., Malik, J.: End-to-end recovery of human shape and pose. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2018)

    2018

  52. [52]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)

    Keetha, N., Karhade, J., Jatavallabhula, K.M., Yang, G., Scherer, S., Ramanan, D., Luiten, J.: Splatam: Splat, track & map 3d gaussians for dense rgb-d slam. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)

    2024

  53. [53]

    ACM Transactions on Graphics42(4) (2023)

    Kerbl, B., Kopanas, G., Leimkühler, T., Drettakis, G.: 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics42(4) (2023)

    2023

  54. [54]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)

    Kim, H., Jang, M., Yoon, W., Lee, J., Na, D., Woo, S.: Switchlight: Co-design of physics-driven architecture and pre-training framework for human portrait relighting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)

    2024

  55. [55]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)

    Kocabas, M., Chang, J.H.R., Gabriel, J., Tuzel, O., Ranjan, A.: HUGS: Human gaussian splats. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)

    2024

  56. [56]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)

    Lassner, C., Zollhöfer, M.: Pulsar: Efficient sphere-based neural rendering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)

    2021

  57. [57]

    In: Advances in Neural Information Processing Systems (NeurIPS) (2024)

    Lee, J., Won, C., Jung, H., Bae, I., Jeon, H.G.: Fully explicit dynamic gaussian splatting. In: Advances in Neural Information Processing Systems (NeurIPS) (2024)

    2024

  58. [58]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)

    Lei, J., Wang, Y., Pavlakos, G., Liu, L., Daniilidis, K.: GART: Gaussian articulated template models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)

    2024

  59. [59]

    In: ACM SIGGRAPH Asia Conference Papers (2024)

    Li, J., Cao, C., Schwartz, G., Khirodkar, R., Richardt, C., Simon, T., Sheikh, Y., Saito, S.: Uravatar: Universal relightable gaussian codec avatars. In: ACM SIGGRAPH Asia Conference Papers (2024)

    2024

  60. [60]

    In: European Conference on Computer Vision (ECCV) (2022)

    Li, R., Tanke, J., Vo, M., Zollhofer, M., Gall, J., Kanazawa, A., Lassner, C.: Tava: Template-free animatable volumetric actors. In: European Conference on Computer Vision (ECCV) (2022)

    2022

  61. [61]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024) 20 A

    Li, Z., Chen, Z., Li, Z., Xu, Y.: Spacetime gaussian feature splatting for real- time dynamic view synthesis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024) 20 A. Mir et al

    2024

  62. [62]

    arXiv preprint arXiv:2311.16096 (2024)

    Li, Z., Sun, Y., Zheng, Z., Wang, L., Zhang, S., Liu, Y.: Animatable and relightable gaussians for high-fidelity human avatar modeling. arXiv preprint arXiv:2311.16096 (2024)

    work page arXiv 2024

  63. [63]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)

    Li, Z., Zheng, Z., Wang, L., Liu, Y.: Animatable gaussians: Learning pose- dependent gaussian maps for high-fidelity human avatar modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)

    2024

  64. [64]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)

    Liang, Z., Zhang, Q., Feng, Y., Shan, Y., Jia, K.: GS-IR: 3D gaussian splatting for inverse rendering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)

    2024

  65. [65]

    ACM Transactions on Graphics (2021)

    Liu, L., Habermann, M., Rudnev, V., Sarkar, K., Gu, J., Theobalt, C.: Neural actor: Neural free-view synthesis of human actors with pose control. ACM Transactions on Graphics (2021)

    2021

  66. [66]

    In: ACM SIGGRAPH Conference Proceedings (2000)

    Lokovic, T., Veach, E.: Deep shadow maps. In: ACM SIGGRAPH Conference Proceedings (2000)

    2000

  67. [67]

    ACM Transactions on Graphics34(6) (2015)

    Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., Black, M.J.: SMPL: A skinned multi-person linear model. ACM Transactions on Graphics34(6) (2015)

    2015

  68. [68]

    In: International Conference on 3D Vision (3DV) (2024)

    Luiten, J., Kopanas, G., Leibe, B., Ramanan, D.: Dynamic 3d gaussians: Tracking by persistent dynamic view synthesis. In: International Conference on 3D Vision (3DV) (2024)

    2024

  69. [69]

    , author Pan, H

    Luo, F., Pan, H., Yang, X., Jiang, B., Liu, F., Huang, T.: ShadowGS: Shadow- aware 3D gaussian splatting for satellite imagery. arXiv preprint arXiv:2601.00939 (2026)

    work page arXiv 2026

  70. [70]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2019)

    Mahmood, N., Ghorbani, N., Troje, N.F., Pons-Moll, G., Black, M.J.: AMASS: Archive of motion capture as surface shapes. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2019)

    2019

  71. [71]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)

    Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., Geiger, A.: Occupancy networks: Learning 3d reconstruction in function space. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)

    2019

  72. [72]

    In: European Conference on Computer Vision (ECCV) (2024)

    Mihajlovic, M., Prokudin, S., Tang, S., Maier, R., Bogo, F., Tung, T., Boyer, E.: SplatFields: Neural gaussian splats for sparse 3d and 4d reconstruction. In: European Conference on Computer Vision (ECCV) (2024)

    2024

  73. [73]

    In: European Conference on Computer Vision (ECCV) (2020)

    Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: Nerf: Representing scenes as neural radiance fields for view synthesis. In: European Conference on Computer Vision (ECCV) (2020)

    2020

  74. [74]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)

    Mir, A., Alldieck, T., Pons-Moll, G.: Learning to transfer texture from clothing images to 3d humans. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)

    2020

  75. [75]

    In: European Conference on Computer Vision (ECCV) (2026)

    Mir, A., Guler, R.A., Tang, X., Wonka, P., Pons-Moll, G.: Ahoy! animatable humans under occlusion from youtube videos with gaussian splatting and video diffusion priors. In: European Conference on Computer Vision (ECCV) (2026)

    2026

  76. [76]

    GASPACHO: Gaus- sian Splatting for Controllable Humans and Objects.arXiv preprint arXiv:2503.09342, 2025

    Mir, A., Moreau, A., Dhamo, H., Zhang, Z., Pons-Moll, G., Pérez-Pellitero, E.: Gaspacho: Gaussian splatting for controllable humans and objects. arXiv preprint arXiv:2503.09342 (2025)

    work page arXiv 2025

  77. [77]

    In: International Conference on 3D Vision (3DV) (2024)

    Mir, A., Puig, X., Kanazawa, A., Pons-Moll, G.: Generating continual human motion in diverse 3d scenes. In: International Conference on 3D Vision (3DV) (2024)

    2024

  78. [78]

    arXiv preprint arXiv:2511.09827 (2025) RAGA: Ray Traced Gaussian Shadow Casting 21

    Mir, A., Wang, J., Guler, R.A., Guo, C., Pons-Moll, G., Zhou, B.: Aha! ani- mating human avatars in diverse scenes with gaussian splatting. arXiv preprint arXiv:2511.09827 (2025) RAGA: Ray Traced Gaussian Shadow Casting 21

    work page arXiv 2025

  79. [79]

    ACM Transactions on Graphics (2024)

    Moenne-Loccoz, N., Mirzaei, A., Perel, O., de Lutio, R., Esturo, J.M., State, G., Fidler, S., Sharp, N., Gojcic, Z.: 3d gaussian ray tracing: Fast tracing of particle scenes. ACM Transactions on Graphics (2024)

    2024

  80. [80]

    In: European Conference on Computer Vision (ECCV) (2024)

    Moon, G., Shiratori, T., Saito, S.: Expressive whole-body 3D gaussian avatar. In: European Conference on Computer Vision (ECCV) (2024)

    2024

Showing first 80 references.