Pith. sign in

REVIEW 4 major objections 5 minor 54 references

PrismAvatar: Real-time animated 3D neural head avatars on edge devices

T0 review · 4 major / 5 minor · reviewed 2026-08-08 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A head avatar trained as a hybrid mesh and neural radiance field can be distilled into a rigged mesh that animates at 60 fps on phones with quality comparable to desktop methods.

desk verdict Genuinely useful new representation for mobile head avatars, but the quality claims outrun the evidence and the prism lattice has a scoping limit the paper never flags. read the letter →

arxiv 2502.07030 v1 pith:5KSXUS5M submitted 2025-02-10 cs.CV cs.GRcs.LG

Prashant Raina , Felix Taubner , Mathieu Tuli , Eu Wern Teh , Kevin Ferreira This is my paper · ORCID
classification cs.CVcs.GRcs.LG
keywords 3Dheadavatarneuralradiancefieldprismlatticemorphablemodeledgedevicesreal-timerenderingmeshdistillationtextures
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

PrismAvatar sets out to make photorealistic 3D head avatars run on phones, tablets, and browser-based edge devices rather than desktop GPUs. It trains a hybrid representation: the FLAME morphable head supplies a rigged mesh surface, while hair and facial hair are modeled by a deformable neural radiance field controlled by a prism lattice extruded from the mesh. After training, the volumetric field is distilled into a rigged triangle mesh with neural textures and a small color network, so animation and rendering use only standard triangle rasterization. The paper reports 60 fps on an iPhone 14 Pro, a fourth-generation iPad Pro, and a Galaxy S9, with an average 70 MB download, and post-export image quality metrics that are competitive with desktop avatar methods.

What carries the argument

The load-bearing object is the prism lattice: every subdivided triangle of a marked FLAME region (scalp, sideburns, mouth) is extruded along its vertex normals in up to 16 stacked layers, and this is repeated for all 400 FLAME shape and expression blendshapes so the lattice becomes part of the morphable model. Extruded vertices inherit the base vertices' linear blend skinning weights and pose-corrective blendshapes, which is what lets a canonical-space NeRF for hair deform with expression and head pose. At render time, a differentiable ray tracer intersects these lattice triangles instead of ray-marching a volume, giving dense sample points in canonical space; opacity and feature prediction use hash-grid-encoded fully fused MLPs, and a two-hidden-layer, 16-neuron MLP maps an 8D feature plus view direction to RGB. At export, alpha and feature values are baked into 16x16 texture cells per kept triangle, with a low-distortion square-to-triangle mapping, and the color MLP becomes the neural shader in the mobile viewer.

What would settle it

Record a monocular video in which the subject shakes or nods the head so hair visibly lags behind the skull, train a PrismAvatar on it, and render a held-out frame from the exported model: if the reconstructed hair smears, blurs, or stays welded to the scalp while the video shows independent motion, the rigid-attachment premise fails. A quantitative version is to compare post-export PSNR on such a sequence against INSTA and check whether the gap is much larger than the reported 2.1 dB.

Watch

Extended reading notes

Core claim

The central claim is that an animatable neural head avatar does not need volumetric rendering at inference time. By subdividing marked scalp and face triangles of the FLAME template and extruding them along vertex normals, once per shape and expression blendshape, the paper obtains a prism lattice that inherits FLAME's skinning weights and moves with the head. Rays that first hit the FLAME mesh sample a learned texture; rays through the lattice are integrated over up to 64 intersections in canonical space, with opacity from a hash-grid MLP, an 8D view-independent feature vector from a second MLP, and RGB color from a small view-conditioned network. A second training stage binarizes opacity and collapses each ray to one weighted feature vector, mirroring the deferred neural rendering used by the exported model. The exported artifact is a rigged mesh with alpha and feature texture maps plus the color network, and the paper reports that it animates at 60 fps in a WebAssembly viewer on the devices listed above, with a post-export PSNR of 30.4 dB on INSTA monocular videos versus 32.5 dB for INSTA.

Load-bearing premise

The method assumes hair moves exactly as the rigged FLAME mesh moves, because the prism lattice is extruded from mesh blendshapes and has no physics of its own, so any hair motion caused by gravity, inertia, or wind will misalign the reconstructed hair.

Editorial extensions

If this is right

  • A personalized avatar can be shipped as a single rigged mesh with textures and a small MLP, so no CUDA, OptiX, or custom volumetric renderer is required on the user's device.
  • Because the view-independent features are baked into texture maps and the color network is tiny, the same exported model can be animated by any WebGL-capable browser, not just high-end phones.
  • Expression-driven hair deformation is included for free: the prism lattice follows the FLAME blendshapes, so blinking, talking, and facial expressions move reconstructed scalp and facial hair consistently with the skin.
  • The two-stage training (volumetric first, then rasterization-mimicking with binarized opacity) means the deployed model is trained with the same approximation it will use at run time, which the reported metrics show costs only a small quality drop.
  • With 70 MB downloads and 60 fps on devices from 2018 onward, animatable avatars become feasible for browser-based teleconferencing and virtual assistants.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The rigid-attachment assumption means hair dynamics are capped by the FLAME deformation model; a natural next step is to add a lightweight physics layer for hair, but that would force the lattice to decouple from the skull.
  • The same extrusion-and-distill recipe should transfer to other animatable regions with complex geometry, such as clothing wrinkles or accessories, by marking extra mesh triangles and building lattices over their blendshapes.
  • Because the reported 60 fps is VSync-capped in the browser, the actual headroom on the iPhone and iPad is unknown; the Galaxy S9 compatibility at 60 fps suggests low-end Android may be the more informative stress test.
  • If the method were evaluated on sequences with fast head rotation or free-flowing hair, the gap between pre-export and post-export metrics would likely widen, giving a quantitative test of where the lattice assumption starts to fail.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. PrismAvatar proposes a pipeline for building animatable 3D head avatars from monocular or multi-view video, targeting real-time rendering on edge devices. At training time the method uses a hybrid model: a FLAME mesh with learned textures for the face, plus a deformable NeRF for hair and other regions not covered by the 3DMM, with deformation controlled by a rigged prism lattice constructed by extruding marked FLAME scalp triangles. A custom differentiable ray-triangle renderer trains the model, after which the NeRF is distilled into a rigged triangle mesh with neural textures and a small color MLP, renderable with ordinary WebGL. The authors report 60 fps on iPhone 14 Pro, iPad Pro, and Galaxy S9, about 70 MB download size, and quantitative metrics in Table 1 comparing before/after export against PointAvatar, FLARE, and INSTA.

Significance. The paper addresses a timely and practically important problem: bringing animatable neural head avatars to resource-constrained devices using only the standard triangle rendering pipeline. The concrete engineering contributions are valuable if the claims hold: the rigged prism lattice ties NeRF deformation to a morphable model, the two-stage hybrid rendering with alpha binarization and export to neural textures is a coherent design, and the reported 60 fps and 70 MB footprint on specific devices are specific, falsifiable numbers. The paper also makes a clear methodological choice to train with volumetric rendering and then distill to a mesh representation. However, the central 'comparable quality' claim is weakened by the paper's own Table 1, which shows a consistent gap to INSTA after export, and the representational scope of the prism lattice is narrower than the text suggests. If these issues are addressed with stronger quantitative evidence and clearer scope statements, the contribution would be solid.

major comments (4)
  1. [Section 3.2 / Section 3.4 / Section 6] The prism lattice is a shell built by subdividing marked scalp triangles and extruding them along vertex normals for up to 16 layers, and rendering samples the canonical NeRF only at ray-lattice intersections. Consequently, any hair that extends outside this shell -- for example long hair hanging below the scalp or strands not aligned with the local scalp normal -- is never sampled and cannot be reconstructed, even in canonical space. The limitations paragraph in Section 6 only mentions physics-based animation such as gravity and does not flag this static geometric bound. Since Table 1 is averaged over monocular INSTA videos without per-subject hairstyle information, the claim that the method reconstructs hair with quality comparable to desktop state-of-the-art is supported only for hairstyles contained in a scalp-offset shell.
  2. [Table 1 / Section 5] The 'comparable quality' claim is not adequately supported by the quantitative evaluation. After export, PrismAvatar reports PSNR 30.4 versus INSTA 32.5, SSIM 0.929 versus 0.953, MS-SSIM 0.960 versus 0.977, and LPIPS 0.069 versus 0.045; this is a consistent gap of roughly 2 dB PSNR and 0.024 LPIPS on the deployed representation. The table has no error bars, per-subject breakdown, or significance test, and the text does not state how many videos are averaged. The authors should report per-subject metrics and variance, and either soften the headline claim or show that the gap is not systematic.
  3. [Section 3.2 / Section 6] The animation of hair is predicated on the assumption that extruded lattice vertices inherit FLAME skinning weights, so all hair motion is rigidly tied to FLAME shape, expression, and skeletal motion. The limitation paragraph only excludes gravity, but any non-rigid hair motion (swinging, inertia, wind, or contact) will misalign the canonical NeRF and cannot be corrected by the current model. This is a correctness-risk concern for the 'animatable hair' claim; a concrete test would be to evaluate on sequences with visible non-rigid hair motion and report alignment quality.
  4. [Section 4 / Table 1] No ablation study isolates the contribution of the main design choices: number of prism lattice layers, subdivision level, texture cell resolution, network widths, or the alpha pruning threshold used in export. Since these parameters are chosen freely and the only quantitative evidence is one aggregated table, it is unclear whether the reported quality is robust or tuned to the particular videos. A small ablation on one or two subjects for at least lattice depth and texture resolution would substantially strengthen the paper.
minor comments (5)
  1. [Throughout] There are several typos: 'view-indepdendent' in Section 3.3 should be 'view-independent'; 'compatiblity' in Section 4.2 should be 'compatibility'; and the reference list has 'San Diega' instead of 'San Diego'.
  2. [Section 5] The device is referred to as 'iPhone Pro 14'; the standard name is 'iPhone 14 Pro', which is used in the abstract and should be used consistently.
  3. [Section 4.3 / Reference [2]] The head tracker FlowFace is cited as 'Authors Anonymous' and is under review, but head tracking is a required preprocessing step. Please provide enough implementation detail or a release for reproducibility.
  4. [Section 5] The multi-view experiments on NeRSemble and RenderMe-360 are mentioned but no quantitative or qualitative results are shown; please clarify their role or add a summary.
  5. [Section 5 / Reproducibility] The paper does not state whether code or trained models will be released; given the systems contribution, a release statement would help reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: PrismAvatar is an empirical systems paper whose trained model is evaluated on held-out frames and external baselines; no load-bearing step reduces to its own inputs.

full rationale

The paper's claimed derivation chain is an analysis-by-synthesis training pipeline, not a closed-form derivation. Inputs are matted images, camera poses, and FLAME parameters from a head tracker; the hybrid model is trained with photometric and silhouette losses; the opacity network is then frozen and binarized; and the model is exported to a rigged mesh with neural textures and evaluated on the original non-overlapping train-test splits of the INSTA videos. The central claims—60 fps on edge devices, ~70 MB download, and image quality comparable to desktop methods—are empirical measurements against held-out data and external baselines (PointAvatar, FLARE, INSTA). No equation in the paper defines a predicted quantity in terms of the same quantity from the training set. The only self-referential component is the citation of 'FlowFace' as an under-review tracker that provides camera and 3DMM parameters; this supplies input parameters rather than proving any output, and it is not used to justify the quality or speed claims. The prism-lattice shell and its representational bound for hair that extends beyond the scalp-offset shell is a real geometric limitation and a correctness risk for long hairstyles, but it is not circularity: the paper claims to reconstruct hair within its lattice representation and evaluates that claim empirically, and a failure on out-of-shell hair would be an empirical failure, not a logical reduction of the result to its inputs. Therefore, no significant circularity is present.

Assumptions & free parameters 6 free parameters · 5 assumptions · 2 invented entities

The central claims are empirical; they rest on the 3DMM, the tracker, the lattice, and the distillation, with several hand-chosen constants. No machine-checked proof or public release is provided.

free parameters (6)
  • Prism lattice layers = up to 16 layers
    Chosen by hand to cover hair volume; affects mesh size and the 60 fps claim.
  • Lattice subdivision = 4 or 16 triangles per base triangle
    Manual choice balancing density and memory.
  • Texture cell resolution = 16x16 samples per triangle
    Determines export texture resolution and VRAM usage.
  • Training steps per stage = 90,000 batches
    Arbitrary; not justified by convergence curves.
  • Network widths = A/F MLPs 4x128; C 2x16
    Standard MobileNeRF-style sizes, chosen by hand.
  • Alpha pruning threshold = 0.5
    Used in export pruning; affects quality and triangle count.
assumptions (5)
  • domain assumption FLAME 3DMM covers face and neck shape and expression; mesh is an adequate surface for the face.
    Section 3.1: 'The mesh representation is based on the FLAME 3D morphable model, which adequately models much of the face and neck.'
  • domain assumption Background matting and head tracking provide accurate masks, cameras, and 3DMM parameters.
    Section 4.3: 'The videos are processed using a head tracker to provide camera intrinsics, camera extrinsics and FLAME 2023 model parameters for every frame.'
  • ad hoc to paper The prism lattice, built by extruding FLAME blendshapes, tracks all hair motion rigidly.
    Section 3.2: 'The extruded vertices share the linear blend skinning weights and pose corrective blendshapes of the base vertices.' The paper itself lists the lack of physics-based animation as a limitation.
  • standard math NeRF volume rendering integral is a valid model for hair opacity and color.
    Section 3.4, equations 4-5; standard volumetric rendering assumption.
  • domain assumption Distillation from NeRF to mesh and textures preserves appearance sufficiently.
    Section 3.6, export; the paper reports a drop in metrics after export (PSNR 30.4 vs 32.0), so this assumption is implicitly relied on.
invented entities (2)
  • Rigged prism lattice
    purpose: Deformable control structure that maps ray intersections from the animated mesh to the canonical NeRF space.
    A novel geometry layer with no external falsifiable prediction; its validity is internal to the method.
  • Triangle soup exported representation
    purpose: Rasterizable proxy for the volumetric hair field on edge devices.
    A design artifact, not a physically motivated entity.

how reviews work

0 comments
Cite this review

Pith. "Pith review of PrismAvatar: Real-time animated 3D neural head avatars on edge devices." pith.science (2026). https://pith.science/paper/5KSXUS5M

@misc{pith2026250207030,
  author       = {Pith},
  title        = {Pith review of: PrismAvatar: Real-time animated 3D neural head avatars on edge devices},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5KSXUS5M}},
  note         = {Machine review of arXiv:2502.07030}
}
read the original abstract

We present PrismAvatar: a 3D head avatar model which is designed specifically to enable real-time animation and rendering on resource-constrained edge devices, while still enjoying the benefits of neural volumetric rendering at training time. By integrating a rigged prism lattice with a 3D morphable head model, we use a hybrid rendering model to simultaneously reconstruct a mesh-based head and a deformable NeRF model for regions not represented by the 3DMM. We then distill the deformable NeRF into a rigged mesh and neural textures, which can be animated and rendered efficiently within the constraints of the traditional triangle rendering pipeline. In addition to running at 60 fps with low memory usage on mobile devices, we find that our trained models have comparable quality to state-of-the-art 3D avatar models on desktop devices.

Figures

Figures reproduced from arXiv: 2502.07030 by the authors.

Figure 1
Figure 1. Our method trains a hybrid mesh-volumetric head avatar model from a given video, which we export to a rigged [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Left: The volumetric field for hair in our hybrid model is defined over a prism lattice, constructed as described in [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Illustration of different scenarios in ray intersec [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: A sample of head avatars reconstructed using our method. [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: Using a prism lattice which covers the face allows us to reconstruct facial hair. Thick dark hair and thin blond hair [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

54 extracted references · 45 canonical work pages

  1. [1]

    Emscripten

    2015. Emscripten. https://emscripten.org

    work page 2015

  2. [2]

    Authors Anonymous. 2024. FlowFace: 3D face tracking from 2D video through iterative dense UV to image flow. Under review (2024)

    work page 2024

  3. [3]

    ShahRukh Athar, Zhixin Shu, and Dimitris Samaras. 2023. Flame-in-nerf: Neu- ral control of radiance fields for free view face animation. In 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG) . IEEE, 1–8

    work page 2023

  4. [4]

    ShahRukh Athar, Zexiang Xu, Kalyan Sunkavalli, Eli Shechtman, and Zhixin Shu. 2022. Rignerf: Fully controllable neural 3d portraits. In Proceedings of the IEEE/CVF conference on Computer Vision and Pattern Recognition . 20364–20373

    work page 2022

  5. [5]

    Benjamin Attal, Jia-Bin Huang, Michael Zollhoefer, Johannes Kopf, and Changil Kim. 2022. Learning Neural Light Fields With Ray-Space Embedding Networks. 2022 IEEE. In CVF Conference on Computer Vision and Pattern Recognition (CVPR) . 19787–19797

    work page 2022

  6. [6]

    Ziqian Bai, Feitong Tan, Zeng Huang, Kripasindhu Sarkar, Danhang Tang, Di Qiu, Abhimitra Meka, Ruofei Du, Mingsong Dou, Sergio Orts-Escolano, et al. 2023. Learning Personalized High Quality Volumetric Head Avatars from Monocular RGB Videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 16890–16900

    work page 2023

  7. [7]

    Thabo Beeler, Bernd Bickel, Paul Beardsley, Bob Sumner, and Markus Gross. 2010. High-quality single-shot capture of facial geometry. In ACM SIGGRAPH 2010 papers. 1–9

    work page 2010

  8. [8]

    Yoshua Bengio, Nicholas Léonard, and Aaron Courville. 2013. Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432 (2013)

    arXiv 2013

Show all 54 references
  1. [9]

    Shrisha Bharadwaj, Yufeng Zheng, Otmar Hilliges, Michael J Black, and Vic- toria Fernandez Abrevaya. 2023. FLARE: Fast Learning of Animatable and Relightable Mesh Avatars. ACM Transactions on Graphics (TOG) 42, 6 (2023), 1–15

    2023

  2. [10]

    V Blanz and T Vetter. 1999. A Morphable Model for the Synthesis of 3D Faces. In 26th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH 1999). ACM Press, 187–194

    1999

  3. [11]

    Derek Bradley, Wolfgang Heidrich, Tiberiu Popa, and Alla Sheffer. 2010. High resolution passive facial performance capture. In ACM SIGGRAPH 2010 papers. 1–10

    2010

  4. [12]

    Chen Cao, Tomas Simon, Jin Kyu Kim, Gabe Schwartz, Michael Zollhoefer, Shun- Suke Saito, Stephen Lombardi, Shih-En Wei, Danielle Belko, Shoou-I Yu, et al

  5. [13]

    Junli Cao, Huan Wang, Pavlo Chemerys, Vladislav Shakhrai, Ju Hu, Yun Fu, Denys Makoviichuk, Sergey Tulyakov, and Jian Ren. 2023. Real-Time Neural Light Field on Mobile Devices. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 8328–8337. Ra...

    2023

  6. [14]

    Zhiqin Chen, Thomas Funkhouser, Peter Hedman, and Andrea Tagliasacchi

  7. [15]

    Sara Fridovich-Keil, Alex Yu, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa. 2022. Plenoxels: Radiance fields without neural net- works. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 5501–5510

    2022

  8. [16]

    Yasutaka Furukawa and Jean Ponce. 2009. Dense 3D motion capture for human faces. In 2009 IEEE conference on computer vision and pattern recognition . IEEE, 1674–1681

    2009

  9. [17]

    Guy Gafni, Justus Thies, Michael Zollhofer, and Matthias Nießner. 2021. Dynamic neural radiance fields for monocular 4D facial avatar reconstruction. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 8649–8658

    2021

  10. [18]

    Stephan J Garbin, Marek Kowalski, Matthew Johnson, Jamie Shotton, and Julien Valentin. 2021. FastNeRF: High-fidelity neural rendering at 200fps. InProceedings of the IEEE/CVF International Conference on Computer Vision . 14346–14355

    2021

  11. [19]

    Philip-William Grassal, Malte Prinzler, Titus Leistner, Carsten Rother, Matthias Nießner, and Justus Thies. 2022. Neural head avatars from monocular rgb videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 18653–18664

    2022

  12. [20]

    Yuan-Chen Guo, Yan-Pei Cao, Chen Wang, Yu He, Ying Shan, and Song-Hai Zhang. 2023. VMesh: Hybrid volume-mesh representation for efficient view synthesis. In SIGGRAPH Asia 2023 Conference Papers . 1–11

    2023

  13. [21]

    Aarush Gupta, Junli Cao, Chaoyang Wang, Ju Hu, Sergey Tulyakov, Jian Ren, and Laszlo Attila Jeni. 2023. LightSpeed: Light and Fast Neural Light Fields on Mobile Devices. In Thirty-seventh Conference on Neural Information Processing Systems

    2023

  14. [22]

    Jon Hasselgren, Jacob Munkberg, Jaakko Lehtinen, Miika Aittala, and Samuli Laine. 2021. Appearance-Driven Automatic 3D Model Simplification. In Euro- graphics Symposium on Rendering

    2021

  15. [23]

    Peter Hedman, Pratul P Srinivasan, Ben Mildenhall, Jonathan T Barron, and Paul Debevec. 2021. Baking neural radiance fields for real-time view synthesis. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 5875– 5884

    2021

  16. [24]

    Eric Heitz. 2019. A Low-Distortion Map Between Triangle and Square. https: //eheitzresearch.wordpress.com/749-2/. (2019)

    2019

  17. [25]

    Taras Khakhulin, Vanessa Sklyarova, Victor Lempitsky, and Egor Zakharov. 2022. Realistic one-shot mesh-based head avatars. InEuropean Conference on Computer Vision. Springer, 345–362

    2022

  18. [26]

    Diederik Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimiza- tion. In International Conference on Learning Representations (ICLR) . San Diega, CA, USA

    2015

  19. [27]

    Tobias Kirschstein, Shenhan Qian, Simon Giebenhain, Tim Walter, and Matthias Nießner. 2023. NeRSemble: Multi-View Radiance Field Reconstruction of Human Heads. ACM Trans. Graph. 42, 4, Article 161 (jul 2023), 14 pages. https://doi.org/ 10.1145/3592455

    2023 doi

  20. [28]

    Tianye Li, Timo Bolkart, Michael. J. Black, Hao Li, and Javier Romero. 2017. Learning a model of facial shape and expression from 4D scans.ACM Transactions on Graphics, (Proc. SIGGRAPH Asia) 36, 6 (2017), 194:1–194:17. https://doi.org/ 10.1145/3130800.3130813

    2017

  21. [29]

    Shanchuan Lin, Linjie Yang, Imran Saleemi, and Soumyadip Sengupta. 2022. Robust high-resolution video matting with temporal guidance. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision . 238–247

    2022

  22. [30]

    David B Lindell, Julien NP Martel, and Gordon Wetzstein. 2021. AutoInt: Auto- matic integration for fast neural volume rendering. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 14556–14565

    2021

  23. [31]

    Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, and Yaser Sheikh. 2019. Neural volumes: learning dynamic renderable volumes from images. ACM Transactions on Graphics (TOG) 38, 4 (2019), 1–14

    2019

  24. [32]

    Stephen Lombardi, Tomas Simon, Gabriel Schwartz, Michael Zollhoefer, Yaser Sheikh, and Jason Saragih. 2021. Mixture of volumetric primitives for efficient neural rendering. ACM Transactions on Graphics (ToG) 40, 4 (2021), 1–13

    2021

  25. [33]

    Shugao Ma, Tomas Simon, Jason Saragih, Dawei Wang, Yuecheng Li, Fernando De La Torre, and Yaser Sheikh. 2021. Pixel codec avatars. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 64–73

    2021

  26. [34]

    Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. 2021. NeRF: Representing scenes as neural radiance fields for view synthesis. Commun. ACM 65, 1 (2021), 99–106

    2021

  27. [35]

    Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. 2022. In- stant neural graphics primitives with a multiresolution hash encoding. ACM Transactions on Graphics (ToG) 41, 4 (2022), 1–15

    2022

  28. [36]

    Jacob Munkberg, Jon Hasselgren, Tianchang Shen, Jun Gao, Wenzheng Chen, Alex Evans, Thomas Müller, and Sanja Fidler. 2022. Extracting Triangular 3D Models, Materials, and Lighting From Images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition...

    2022

  29. [37]

    Dongwei Pan, Long Zhuo, Jingtan Piao, Huiwen Luo, Wei Cheng, Yuxin Wang, Siming Fan, Shengqi Liu, Lei Yang, Bo Dai, Ziwei Liu, Chen Change Loy, Chen Qian, Wayne Wu, Dahua Lin, and Kwan-Yee Lin. 2023. RenderMe-360: Large Digital Asset Library and Benchmark Towards High-fidelity...

    2023

  30. [38]

    Christian Reiser, Songyou Peng, Yiyi Liao, and Andreas Geiger. 2021. Kilonerf: Speeding up neural radiance fields with thousands of tiny mlps. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 14335–14345

    2021

  31. [39]

    Justus Thies, Michael Zollhöfer, and Matthias Nießner. 2019. Deferred neural rendering: Image synthesis using neural textures. Acm Transactions on Graphics (TOG) 38, 4 (2019), 1–12

    2019

  32. [40]

    Cong Wang, Di Kang, Yan-Pei Cao, Linchao Bao, Ying Shan, and Song-Hai Zhang

  33. [41]

    Huan Wang, Jian Ren, Zeng Huang, Kyle Olszewski, Menglei Chai, Yun Fu, and Sergey Tulyakov. 2022. R2L: Distilling neural radiance field to neural light field for efficient novel view synthesis. In European Conference on Computer Vision . Springer, 612–629

    2022

  34. [42]

    Peng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt, Taku Komura, and Wen- ping Wang. 2021. NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction. Advances in Neural Information Processing Sys- tems 34 (2021), 27171–27183

    2021

  35. [43]

    In SIGGRAPH Asia 2023 Conference Papers

    Neural point-based volumetric avatar: Surface-guided neural points for efficient and photorealistic volumetric head avatar. In SIGGRAPH Asia 2023 Conference Papers. 1–12

    2023

  36. [44]

    Yuelang Xu, Hongwen Zhang, Lizhen Wang, Xiaochen Zhao, Huang Han, Qi Guojun, and Yebin Liu. 2023. LatentAvatar: Learning latent expression code for expressive neural head avatar. In ACM SIGGRAPH 2023 Conference Proceedings

    2023

  37. [45]

    Alex Yu, Ruilong Li, Matthew Tancik, Hao Li, Ren Ng, and Angjoo Kanazawa

  38. [46]

    Zian Wang, Tianchang Shen, Merlin Nimier-David, Nicholas Sharp, Jun Gao, Alexander Keller, Sanja Fidler, Thomas Müller, and Zan Gojcic. 2023. Adaptive Shells for Efficient Neural Radiance Field Rendering. ACM Trans. Graph. 42, 6, Article 259 (2023), 15 pages. https://doi.org/1...

    2023 doi

  39. [47]

    Heng Yu, Joel Julin, Zoltan A Milacski, Koichiro Niinuma, and László A Jeni. 2023. DyLiN: Making Light Field Networks Dynamic. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 12397–12406

    2023

  40. [48]

    Yufeng Zheng, Victoria Fernández Abrevaya, Marcel C Bühler, Xu Chen, Michael J Black, and Otmar Hilliges. 2022. IM Avatar: Implicit morphable head avatars from videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 13545–13555

    2022

  41. [49]

    Yufeng Zheng, Wang Yifan, Gordon Wetzstein, Michael J Black, and Otmar Hilliges. 2023. PointAvatar: Deformable point-based head avatars from videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog- nition. 21057–21067

    2023

  42. [50]

    Changqian Yu, Jingbo Wang, Chao Peng, Changxin Gao, Gang Yu, and Nong Sang. 2018. BiSeNet: Bilateral segmentation network for real-time semantic segmentation. In European Conference on Computer Vision . Springer, 334–349

    2018

  43. [54]

    Wojciech Zielonka, Timo Bolkart, and Justus Thies. 2023. Instant volumetric head avatars. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 4574–4584

    2023

  44. [2021]

    InProceedings of the IEEE/CVF International Conference on Computer Vision

    Plenoctrees for real-time rendering of neural radiance fields. InProceedings of the IEEE/CVF International Conference on Computer Vision . 5752–5761

  45. [2022]

    ACM Transactions on Graphics (TOG) 41, 4 (2022), 1–19

    Authentic volumetric avatars from a phone scan. ACM Transactions on Graphics (TOG) 41, 4 (2022), 1–19

    2022

  46. [2023]

    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    MobileNeRF: Exploiting the polygon rasterization pipeline for efficient neural field rendering on mobile architectures. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 16569–16578

Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.