6 papers
More Methods, Better Agents? A Reality Check on Self-Distillation in Agentic Reinforcement Learning
Linhao Zhong, Bozhen Fang, Xuehao Wang +7
Self-distillation in agentic reinforcement learning has produced a growing number of methods. However, existing evaluations often reuse published baseline scores under differing ex…
Eliciting Complex Spatial Reasoning in MLLMs through Wide-Baseline Matching
Hao Zhong, Muzhi Zhu, Shenyan Zeng +8
Wide-baseline matching (WBM) requires integrating geometric understanding, viewpoint changes, fine-grained perception, and occlusion reasoning, making it a challenging testbed for…
Scalable Adaptation of 3D Geometric Foundation Models via Weak Supervision from Internet Video
Zihui Gao, Ke Liu, Donny Y. Chen +5
Geometric foundation models show promise in 3D reconstruction. However, their progress is severely constrained by the scarcity of diverse, large-scale 3D annotations. While static-…
DriveGen3D: Boosting Feed-Forward Driving Scene Generation with Efficient Video Diffusion
Weijie Wang, Jiagang Zhu, Zeyu Zhang +14
We present DriveGen3D, a novel framework for generating high-quality and highly controllable dynamic 3D driving scenes that addresses critical limitations in existing methodologies…
Revisiting Depth Representations for Feed-Forward 3D Gaussian Splatting
Duochao Shi, Weijie Wang, Donny Y. Chen +4
Depth maps are widely used in feed-forward 3D Gaussian Splatting (3DGS) pipelines by unprojecting them into 3D point clouds for novel view synthesis. This approach offers advantage…
ZPressor: Bottleneck-Aware Compression for Scalable Feed-Forward 3DGS
Weijie Wang, Donny Y. Chen, Zeyu Zhang +3
Feed-forward 3D Gaussian Splatting (3DGS) models have recently emerged as a promising solution for novel view synthesis, enabling one-pass inference without the need for per-scene…