10 papers
SLARM: Streaming and Language-Aligned Reconstruction Model for Dynamic Scenes
Zhicheng Qiu, Jiarui Meng, Tong-an Luo +4
We propose SLARM, a feed-forward model that unifies dynamic scene reconstruction, semantic understanding, and real-time streaming inference. SLARM captures complex, non-uniform mot…
RobustSCI: Beyond Reconstruction to Restoration for Snapshot Compressive Imaging under Real-World Degradations
Hao Wang, Zhankuo Xu, Jiong Ni +3
Deep learning algorithms for video Snapshot Compressive Imaging (SCI) have achieved great success, yet they predominantly focus on reconstructing from clean measurements. This over…
Breaking the Vicious Cycle: Coherent 3D Gaussian Splatting from Sparse and Motion-Blurred Views
Zhankuo Xu, Chaoran Feng, Yingtao Li +5
3D Gaussian Splatting (3DGS) has emerged as a state-of-the-art method for novel view synthesis. However, its performance heavily relies on dense, high-quality input imagery, an ass…
RigAnything: Template-Free Autoregressive Rigging for Diverse 3D Assets
Isabella Liu, Zhan Xu, Wang Yifan +5
We present RigAnything, a novel autoregressive transformer-based model, which makes 3D assets rig-ready by probabilistically generating joints and skeleton topologies and assigning…
Generating, Fast and Slow: Scalable Parallel Video Generation with Video Interface Networks
Bhishma Dedhia, David Bourgin, Krishna Kumar Singh +5
Diffusion Transformers (DiTs) can generate short photorealistic videos, yet directly training and sampling longer videos with full attention across the video remains computationall…
Video Motion Graphs
Haiyang Liu, Zhan Xu, Fa-Ting Hong +3
We present Video Motion Graphs, a system designed to generate realistic human motion videos. Using a reference video and conditional signals such as music or motion tags, the syste…