4 papers
Explicit Critic Guidance for Aligning Diffusion Models
Zhengyang Liang, Qihang Zhang, Ceyuan Yang
Online reinforcement learning is becoming increasingly important for aligning diffusion models with non-differentiable objectives. However, existing methods still face limitations…
DART: Denoising Autoregressive Transformer for Scalable Text-to-Image Generation
Jiatao Gu, Yuyang Wang, Yizhe Zhang +5
Diffusion models have become the dominant approach for visual generation. They are trained by denoising a Markovian process which gradually adds noise to the input. We argue that t…
DSplats: 3D Generation by Denoising Splats-Based Multiview Diffusion Models
Kevin Miao, Harsh Agrawal, Qihang Zhang +4
Generating high-quality 3D content requires models capable of learning robust distributions of complex scenes and the real-world objects within them. Recent Gaussian-based 3D recon…
World-consistent Video Diffusion with Explicit 3D Modeling
Qihang Zhang, Shuangfei Zhai, Miguel Angel Bautista +4
Recent advancements in diffusion models have set new benchmarks in image and video generation, enabling realistic visual synthesis across single- and multi-frame contexts. However,…