8 citations · 12 across the 10 of their papers we have counts for
11 papers · 1 filter
DiffMoE: Dynamic Token Selection for Scalable Diffusion Transformers
Minglei Shi, Ziyang Yuan, Haotian Yang +10
Diffusion models have demonstrated remarkable success in various image generation tasks, but their performance is often limited by the uniform processing of inputs across varying c…
Boosting Resolution Generalization of Diffusion Transformers with Randomized Positional Encodings
Liang Hou, Cong Liu, Mingwu Zheng +4
Resolution generalization in image generation tasks enables the production of higher-resolution images with lower training resolution overhead. However, a key obstacle for diffusio…
Improving Video Generation with Human Feedback
Jie Liu, Gongye Liu, Jiajun Liang +14
Video generation has achieved significant advances through rectified flow techniques, but issues like unsmooth motion and misalignment between videos and prompts persist. In this w…
Towards Precise Scaling Laws for Video Diffusion Transformers
Yuanyang Yin, Yaqi Zhao, Mingwu Zheng +11
Achieving optimal performance of video diffusion transformers within given data and compute budget is crucial due to their high training costs. This necessitates precisely determin…
Koala-36M: A Large-scale Video Dataset Improving Consistency between Fine-grained Conditions and Video Content
Qiuheng Wang, Yukai Shi, Jiarong Ou +10
With the continuous progress of visual generation technologies, the scale of video datasets has grown exponentially. The quality of these datasets plays a pivotal role in the perfo…
VRMM: A Volumetric Relightable Morphable Head Model
Haotian Yang, Mingwu Zheng, Chongyang Ma +3
In this paper, we introduce the Volumetric Relightable Morphable Model (VRMM), a novel volumetric and parametric facial prior for 3D face modeling. While recent volumetric prior mo…
I2V-Adapter: A General Image-to-Video Adapter for Diffusion Models
Xun Guo, Mingwu Zheng, Liang Hou +9
Text-guided image-to-video (I2V) generation aims to generate a coherent video that preserves the identity of the input image and semantically aligns with the input prompt. Existing…
ImFace++: A Sophisticated Nonlinear 3D Morphable Face Model with Implicit Neural Representations
Mingwu Zheng, Haiyu Zhang, Hongyu Yang +2
Accurate representations of 3D faces are of paramount importance in various computer vision and graphics applications. However, the challenges persist due to the limitations impose…
Towards Practical Capture of High-Fidelity Relightable Avatars
Haotian Yang, Mingwu Zheng, Wanquan Feng +5
In this paper, we propose a novel framework, Tracking-free Relightable Avatar (TRAvatar), for capturing and reconstructing high-fidelity 3D avatars. Compared to previous methods, T…
NeuFace: Realistic 3D Neural Face Rendering from Multi-view Images
Mingwu Zheng, Haiyu Zhang, Hongyu Yang +1
Realistic face rendering from multi-view images is beneficial to various computer vision and graphics applications. Due to the complex spatially-varying reflectance properties and…
ImFace: A Nonlinear 3D Morphable Face Model with Implicit Neural Representations
Mingwu Zheng, Hongyu Yang, Di Huang +1
Precise representations of 3D faces are beneficial to various computer vision and graphics applications. Due to the data discretization and model linearity, however, it remains cha…