arXiv 2610.21302 · cs.CV · 2026-10-15
Dusting the Phantom: Gaussian Allocation for Fading Dynamic Content
Minhyeok Roh, Minseo Lee, Jun Young Jeong, Byeonghyeon Lee, Lucas Yunkyu Lee, Jaesik Park, Eunbyung Park
Affiliation unverified
In latest · #389 Added on 2026-10-25
One-line takeaway
For dynamic Gaussian volumetric video, where small fast objects vanish, the authors argue the bottleneck is where and when Gaussians are allocated, not capacity, and propose allocation that accounts for primitives' spatiotemporal destinations.
针对动态高斯体积视频里小而快的物体消失、细节丢失,作者认为瓶颈在于高斯基元在何时何地分配,而非容量,并提出考虑基元时空去向的新分配策略。
Read on arXiv PDF Hugging Face discussion Code Project page
Topics & disciplines
More on Vision & 3D
- Spectral-Spatial Interpretation ▲753
- VisionHOPE: Visual Backbones as Self-Modifying Learning Systems ▲296
- EgoXperience: Learning World-Grounded Egocentric Representations from Human Experience ▲189
- Holi-World: Building Holistic World Intelligence from Human-Ego Experience ▲172
- DONIC: Generalist Dexterous Bimanual Tracking Control from Human Demonstrations ▲161
More on Visual generation
- StreamEval: Evaluating Interactive Streaming Video Generation Models 2026-10-20 · ▲649
- MaLiang-Harness: A Programmable Path to Image and Video Generation 2026-09-28 · ▲367
- Any Time, Any Space: A World-State-Centric Framework for Agentic Video Generation 2026-10-14 · ▲364
- DiffusionART: Efficient, Stable and Scalable Reinforcement of Diffusion Models 2026-10-08 · ▲325
- AgentGarten: Code Worlds for Evolving Agents 2026-10-08 · ▲293
About this data
Metadata from the arXiv API (verified); upvotes from Hugging Face Daily Papers; citations from Semantic Scholar. Last refreshed 2026-10-26.
The takeaway is written by AIMap from the abstract; the abstract itself is not reproduced — read the original.