{"total":12,"items":[{"citing_arxiv_id":"2606.00452","ref_index":26,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Beyond Static Gaussians: An Empirical Investigation of Architectural Paradigms for Dynamic 3D Scene Reconstruction","primary_cat":"cs.CV","submitted_at":"2026-05-30T00:42:38+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Structure-guided dynamic 3DGS methods deliver superior reconstruction fidelity and compactness on D-NeRF while gaussian-centric methods provide higher rendering speeds at the cost of quality variability and storage.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.00444","ref_index":25,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Real-Time Physics Simulation with Dynamic Mesh-Gaussian Reconstructions","primary_cat":"cs.CV","submitted_at":"2026-05-30T00:30:11+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Dual-representation framework pairs fixed-topology meshes for physics with Gaussian splatting for rendering, but two conversion strategies from varying-topology reconstructions cause 65-80% geometric degradation and underperform native fixed-topology methods.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.25909","ref_index":12,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"R5DGS: Semantic-Aware 4D Gaussian Splatting with Rigid Body Constraints for Efficient Dynamic Scene Reconstruction","primary_cat":"cs.CV","submitted_at":"2026-05-25T14:46:04+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"R5DGS augments physics-driven 4D Gaussian splatting with identity encodings and centroid-only rigid-body dynamics to enable semantic open-vocabulary retrieval and 11 FPS faster extrapolation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.23672","ref_index":57,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"RiGS: Rigid-aware 4D Gaussian Splatting from a Single Monocular Video","primary_cat":"cs.CV","submitted_at":"2026-05-22T14:20:39+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"RiGS decomposes scenes into static, rigid, and transient 4D Gaussians with an object-wise dynamic mask and scene flow guidance to model multi-scale motions and achieve SOTA novel view synthesis.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.22190","ref_index":68,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"No Pose, No Problem in 4D: Feed-Forward Dynamic Gaussians from Unposed Multi-View Videos","primary_cat":"cs.CV","submitted_at":"2026-05-21T08:57:21+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"NoPo4D is the first feed-forward system for dynamic 4D Gaussian splatting from unposed multi-view videos, using velocity decomposition supervised by optical flow and a bidirectional motion encoder.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.04527","ref_index":107,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Velox: Learning Representations of 4D Geometry and Appearance","primary_cat":"cs.CV","submitted_at":"2026-05-06T06:12:19+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Velox compresses dynamic point clouds into latent tokens that support geometry via 4D surface modeling and appearance via 3D Gaussians, showing strong results on video-to-4D generation, tracking, and image-to-4D cloth simulation.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"SV4D: Dynamic 3d content genera- tion with multi-frame and multi-view consistency.arXiv preprint arXiv:2407.17470, 2024. 2, 4, 8 [106] Guandao Yang, Xun Huang, Zekun Hao, Ming-Yu Liu, Serge Belongie, and Bharath Hariharan. PointFlow: 3D point cloud generation with continuous normalizing flows. InIEEE International Conference on Computer Vision (ICCV), 2019. 2 [107] Ziyi Yang, Xinyu Gao, Wen Zhou, Shaohui Jiao, Yuqing Zhang, and Xiaogang Jin. Deformable 3d gaussians for high-fidelity monocular dynamic scene reconstruction. arXiv preprint arXiv:2309.13101, 2023. 2 [108] Chun-Han Yao, Yiming Xie, Vikram V oleti, Huaizu Jiang, and Varun Jampani. SV4D2.0: Enhancing spatio-temporal consistency in multi-view video diffusion for high-quality"},{"citing_arxiv_id":"2604.10809","ref_index":115,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"WARPED: Wrist-Aligned Rendering for Robot Policy Learning from Egocentric Human Demonstrations","primary_cat":"cs.RO","submitted_at":"2026-04-12T20:40:59+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"WARPED synthesizes realistic wrist-view observations from monocular egocentric human videos via foundation models, hand-object tracking, retargeting, and Gaussian Splatting to train visuomotor policies that match teleoperation success rates on five tabletop tasks with 5-8x less collection effort.","context_count":1,"top_context_role":"other","top_context_polarity":"unclear","context_text":"Motiontrans: Human vr data enable motion-level learning for robotic manipulation policies.arXiv preprint arXiv:2509.17759, 2025. [114] Yanjie Ze, Gu Zhang, Kangning Zhang, Chenyuan Hu, Muhan Wang, and Huazhe Xu. 3d diffusion policy: Generalizable visuomotor policy learning via simple 3d representations. InProceedings of Robotics: Science and Systems (RSS), 2024. [115] Qiyuan Zeng, Chengmeng Li, Jude St. John, Zhongyi Zhou, Junjie Wen, Guorui Feng, Yichen Zhu, and Yi Xu. Activeumi: Robotic manipulation with active perception from robot-free human demonstrations, 2025. URL https://arxiv.org/abs/2510.01607. [116] Jason Y . Zhang, Sam Pepose, Hanbyul Joo, Deva Ra- manan, Jitendra Malik, and Angjoo Kanazawa. Per- ceiving 3d human-object spatial arrangements from a"},{"citing_arxiv_id":"2604.08547","ref_index":74,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"GaussiAnimate: Reconstruct and Rig Animatable Categories with Level of Dynamics","primary_cat":"cs.CV","submitted_at":"2026-04-09T17:59:59+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Skelebones compresses 4D Gaussian shapes into compact, controllable bones and skeletons, delivering 17.3% PSNR gains over LBS and 21.7% over BoB for unseen poses while preserving reconstruction quality.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"improvement over robust LBS and outperforming GRU- and MLP-based learning methods by >20%. Code will be made publicly available for research purposes atcookmaker.cn/gaussianimate. Keywords:Character Rigging and Skinning·Guassian-based 4D Recon- struction·Motion Matching·Motion Retargeting·Non-rigid Deformation 1 Introduction 3D Gaussian Splatting (3DGS) [29] and its dynamic extensions [74], have emerged as powerful representations for high-fidelity reconstruction and photorealistic rendering, while remaining efficient in both training and inference. As Gaussian assets proliferate, they promise to become foundational building blocks for immersive experiences in gaming, virtual reality, and film. Beyond entertainment, they offer potential for embodied AI, where photorealistic 3D virtual environments"},{"citing_arxiv_id":"2604.06358","ref_index":40,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"GS-Surrogate: Deformable Gaussian Splatting for Parameter Space Exploration of Ensemble Simulations","primary_cat":"cs.GR","submitted_at":"2026-04-07T18:37:15+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"GS-Surrogate creates a canonical Gaussian field that is sequentially deformed by simulation parameters to enable real-time, controllable 3D exploration of ensemble data while separating simulation variations from visualization adjustments.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2512.23180","ref_index":58,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"GaussianDWM: 3D Gaussian Driving World Model for Unified Scene Understanding and Multi-Modal Generation","primary_cat":"cs.CV","submitted_at":"2025-12-29T03:40:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"GaussianDWM uses 3D Gaussians with embedded linguistic features, language-guided sampling, and dual-condition generation for unified scene understanding and multi-modal output in driving world models.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2511.00503","ref_index":96,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Diff4Splat: Controllable 4D Scene Generation with Latent Dynamic Reconstruction Models","primary_cat":"cs.CV","submitted_at":"2025-11-01T11:16:25+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A feed-forward video latent transformer that predicts time-varying 3D Gaussian primitives from one image to produce controllable 4D scenes with appearance, geometry, and motion.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2506.18601","ref_index":84,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"BulletGen: Improving 4D Reconstruction with Bullet-Time Generation","primary_cat":"cs.GR","submitted_at":"2025-06-23T13:03:42+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"BulletGen enhances 4D dynamic scene reconstruction from monocular videos by supervising Gaussian optimization with diffusion-generated frames aligned at a bullet-time step, achieving SOTA on novel-view synthesis and tracking.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}