1 citations · 1 across the 10 of their papers we have counts for
12 papers
Imaging-101: Benchmarking LLM Coding Agents on Scientific Computational Imaging
Siyi Chen, Jiahe Ying, Yixuan Jia +9
Computational imaging, which recovers hidden signals from indirect, noisy measurements, underpins quantitative discovery across scientific disciplines, yet building a correct recon…
Unbiased Diffusion Variational Inversion via Principled Posterior Matching
Weimin Bai, Yuxuan Gu, Yifei Wang +2
Existing score-based methods for inverse problems often resort to approximate minimization of the KL divergence between the inversion distribution and the Bayesian posterior. Such…
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…
Learning Diffusion Model from Noisy Measurement using Principled Expectation-Maximization Method
Weimin Bai, Weiheng Tang, Enze Ye +3
Diffusion models have demonstrated exceptional ability in modeling complex image distributions, making them versatile plug-and-play priors for solving imaging inverse problems. How…
Integrating Amortized Inference with Diffusion Models for Learning Clean Distribution from Corrupted Images
Yifei Wang, Weimin Bai, Weijian Luo +2
Diffusion models (DMs) have emerged as powerful generative models for solving inverse problems, offering a good approximation of prior distributions of real-world image data. Typic…
Blind Inversion using Latent Diffusion Priors
Weimin Bai, Siyi Chen, Wenzheng Chen +1
Diffusion models have emerged as powerful tools for solving inverse problems due to their exceptional ability to model complex prior distributions. However, existing methods predom…
An Expectation-Maximization Algorithm for Training Clean Diffusion Models from Corrupted Observations
Weimin Bai, Yifei Wang, Wenzheng Chen +1
Diffusion models excel in solving imaging inverse problems due to their ability to model complex image priors. However, their reliance on large, clean datasets for training limits…