Ph.D. student at HKUST.
My research interest includes 2D/3D Generation, digital human, neural rendering and contrastive learning.
I love fishing very much !!!
Excited to share our latest work AvatarPointillist! 🚀 We propose an AutoRegressive framework for 4D Gaussian Avatarization, enabling high-quality, animatable digital humans from sparse data.
#CVPR
project page: https://t.co/rnxj0Dj5u8
code: https://t.co/3gjI5EOk44
Excited to share our latest work AvatarPointillist! 🚀 We propose an AutoRegressive framework for 4D Gaussian Avatarization, enabling high-quality, animatable digital humans from sparse data.
#CVPR
project page: https://t.co/rnxj0Dj5u8
code: https://t.co/3gjI5EOk44
We introduce AvatarArtist: Open-Domain 4D Avatarization, a novel single-image to 4D avatar method for diverse styles & domains. Check out our project page and code for more details! 🚀🔥 #CVPR2025#Avatar
Project page: https://t.co/aFUS8XYUfP
code: https://t.co/Fx5z0PxAiF
Our paper AvatarArtist is now live on Hugging Face! 🎉
Try the demo: upload your own photo & generate a 4D avatar in different styles 🎭✨
So many fun ways to play — check it out now!👇
https://t.co/k17QDqmeSu
Follow-Your-Emoji is a diffusion-based framework for portrait animation which animates a reference portrait with target landmark sequences. The main challenge of portrait animation is to preserve the identity of the reference portrait and transfer the target expression to this portrait while maintaining temporal consistency and fidelity.
Paper: Follow-Your-Emoji: Fine-Controllable and Expressive Freestyle Portrait Animation
Link: https://t.co/xvWZjX5mBi
Project: https://t.co/T6UbtvabN8
#AI #AI美女 #LLMs #deeplearning #machinelearning #GenerativeAI
An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion
discuss: https://t.co/rLg75oxUz4
We introduce a new approach for generating realistic 3D models with UV maps through a representation termed "Object Images." This approach encapsulates surface geometry, appearance, and patch structures within a 64x64 pixel image, effectively converting complex 3D shapes into a more manageable 2D format. By doing so, we address the challenges of both geometric and semantic irregularity inherent in polygonal meshes. This method allows us to use image generation models, such as Diffusion Transformers, directly for 3D shape generation. Evaluated on the ABO dataset, our generated shapes with patch structures achieve point cloud FID comparable to recent 3D generative models, while naturally supporting PBR material generation.
We changed "a DSLR portrait of a young man with a muscular jawline, stubble bread" to "a DSLR portrait of a young man with a muscular jawline, stubble bread, he has happy expression"
HeadArtist: Text-conditioned 3D Head Generation with Self Score Distillation.
paper page: https://t.co/hebHQ6VK5K
Project page: https://t.co/mIbyqk95i5
We propose Self Score Distillation to generate and edit high-fidelity 3D heads under text guidance.