11 citations · 13 across the 7 of their papers we have counts for
8 papers
Vidu S2: Real-Time Interactive, Editable, and Spatial Video Generation
Jintao Zhang, Kai Jiang, Jintao Chen +32
We present Vidu S2, which comprises Vidu S2-Avatar, a real-time interactive digital-character model, and Vidu S2-Editing, a real-time video editing model. Moreover, we explore the…
Spectrum Matching: a Unified Perspective for Superior Diffusability in Latent Diffusion
Mang Ning, Mingxiao Li, Le Zhang +4
In this paper, we study the diffusability (learnability) of variational autoencoders (VAEs) in latent diffusion. We first derive distribution-free bounds on the maximum achievable…
LUMA: Low-Dimension Unified Motion Alignment with Dual-Path Anchoring for Text-to-Motion Diffusion Model
Haozhe Jia, Wenshuo Chen, Yuqi Lin +8
While current diffusion-based models, typically built on U-Net architectures, have shown promising results on the text-to-motion generation task, they still suffer from semantic mi…
Consistent Story Generation: Unlocking the Potential of Zigzag Sampling
Mingxiao Li, Mang Ning, Marie-Francine Moens
Text-to-image generation models have made significant progress in producing high-quality images from textual descriptions, yet they continue to struggle with maintaining subject co…
DCTdiff: Intriguing Properties of Image Generative Modeling in the DCT Space
Mang Ning, Mingxiao Li, Jianlin Su +6
This paper explores image modeling from the frequency space and introduces DCTdiff, an end-to-end diffusion generative paradigm that efficiently models images in the discrete cosin…
Representation Learning and Identity Adversarial Training for Facial Behavior Understanding
Mang Ning, Albert Ali Salah, Itir Onal Ertugrul
Facial Action Unit (AU) detection has gained significant attention as it enables the breakdown of complex facial expressions into individual muscle movements. In this paper, we rev…
Elucidating the Exposure Bias in Diffusion Models
Mang Ning, Mingxiao Li, Jianlin Su +2
Diffusion models have demonstrated impressive generative capabilities, but their \textit{exposure bias} problem, described as the input mismatch between training and sampling, lack…
Input Perturbation Reduces Exposure Bias in Diffusion Models
Mang Ning, Enver Sangineto, Angelo Porrello +2
Denoising Diffusion Probabilistic Models have shown an impressive generation quality, although their long sampling chain leads to high computational costs. In this paper, we observ…