1 citations · 1 across the 9 of their papers we have counts for
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InstantViR: Real-Time Video Inverse Problem Solver with Distilled Diffusion Prior
Weimin Bai, Suzhe Xu, Yiwei Ren +4
Video inverse problems are fundamental to streaming, telepresence, and AR/VR, where high perceptual quality must coexist with tight latency constraints. Diffusion-based priors curr…
Masked Auto-Regressive Variational Acceleration: Fast Inference Makes Practical Reinforcement Learning
Yuxuan Gu, Weimin Bai, Yifei Wang +2
Masked auto-regressive diffusion models (MAR) benefit from the expressive modeling ability of diffusion models and the flexibility of masked auto-regressive ordering. However, vani…
Let Language Constrain Geometry: Vision-Language Models as Semantic and Spatial Critics for 3D Generation
Weimin Bai, Yubo Li, Weijian Luo +4
Text-to-3D generation has advanced rapidly, yet state-of-the-art models, encompassing both optimization-based and feed-forward architectures, still face two fundamental limitations…
Vision-Language Models as Differentiable Semantic and Spatial Rewards for Text-to-3D Generation
Weimin Bai, Yubo Li, Weijian Luo +2
Score Distillation Sampling (SDS) enables high-quality text-to-3D generation by supervising 3D models through the denoising of multi-view 2D renderings, using a pretrained text-to-…
Dive3D: Diverse Distillation-based Text-to-3D Generation via Score Implicit Matching
Weimin Bai, Yubo Li, Wenzheng Chen +2
Distilling pre-trained 2D diffusion models into 3D assets has driven remarkable advances in text-to-3D synthesis. However, existing methods typically rely on Score Distillation Sam…
Uni-Instruct: One-step Diffusion Model through Unified Diffusion Divergence Instruction
Yifei Wang, Weimin Bai, Colin Zhang +3
In this paper, we unify more than 10 existing one-step diffusion distillation approaches, such as Diff-Instruct, DMD, SIM, SiD, -distill, etc, inside a theory-driven framework w…