736 citations · 1.1k across the 52 of their papers we have counts for
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Motion Flow Matching for Human Motion Synthesis and Editing
Vincent Tao Hu, Wenzhe Yin, Pingchuan Ma +7
Human motion synthesis is a fundamental task in computer animation. Recent methods based on diffusion models or GPT structure demonstrate commendable performance but exhibit drawba…
Guided Diffusion from Self-Supervised Diffusion Features
Vincent Tao Hu, Yunlu Chen, Mathilde Caron +3
Guidance serves as a key concept in diffusion models, yet its effectiveness is often limited by the need for extra data annotation or classifier pretraining. That is why guidance w…
Boosting Latent Diffusion with Flow Matching
Johannes Schusterbauer, Ming Gui, Pingchuan Ma +4
Visual synthesis has recently seen significant leaps in performance, largely due to breakthroughs in generative models. Diffusion models have been a key enabler, as they excel in i…
State of the Art on Diffusion Models for Visual Computing
Ryan Po, Wang Yifan, Vladislav Golyanik +15
The field of visual computing is rapidly advancing due to the emergence of generative artificial intelligence (AI), which unlocks unprecedented capabilities for the generation, edi…
SceneGenie: Scene Graph Guided Diffusion Models for Image Synthesis
Azade Farshad, Yousef Yeganeh, Yu Chi +3
Text-conditioned image generation has made significant progress in recent years with generative adversarial networks and more recently, diffusion models. While diffusion models con…