works on

From the 1 of 25 linked papers with an AI index.

activity
20242026
collaborators
Showing cs.CVShow all

15 papers · 1 filter

cs.CV2026

Towards Interactive Video World Modeling: Frontiers, Challenges, Benchmarks, and Future Trends

Jiuming Liu, Chaojun Ni, Mengmeng Liu +7

With rapid development of large language models and diffusion-based content generation, world modeling has attracted increasing research attention, benefiting various downstream do…

cs.CV2026

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…

cs.CV2026

ReconPhys: Reconstruct Appearance and Physical Attributes from Single Video

Boyuan Wang, Xiaofeng Wang, Yongkang Li +9

Reconstructing non-rigid objects with physical plausibility remains a significant challenge. Existing approaches leverage differentiable rendering for per-scene optimization, recov…

cs.CV2026

GigaWorld-Policy: An Efficient Action-Centered World--Action Model

Angen Ye, Boyuan Wang, Chaojun Ni +21

World-Action Models (WAM) initialized from pre-trained video generation backbones have demonstrated remarkable potential for robot policy learning. However, existing approaches fac…

cs.CV2026

GigaBrain-0.5M*: a VLA That Learns From World Model-Based Reinforcement Learning

GigaBrain Team, Boyuan Wang, Bohan Li +23

Vision-language-action (VLA) models that directly predict multi-step action chunks from current observations face inherent limitations due to constrained scene understanding and we…

cs.CV2025

SwiftVLA: Unlocking Spatiotemporal Dynamics for Lightweight VLA Models at Minimal Overhead

Chaojun Ni, Cheng Chen, Xiaofeng Wang +12

Vision-Language-Action (VLA) models built on pretrained Vision-Language Models (VLMs) show strong potential but are limited in practicality due to their large parameter counts. To…