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IDEAS TAKING SHAPE

013D avatars
02Diffusion models
03Energy-based learning

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Featured Publications

FastAvatar: Towards Unified Fast High-Fidelity 3D Avatar Reconstruction with Large Gaussian Reconstruction Transformers

This paper introduces FastAvatar, a method for fast high-fidelity 3D avatar reconstruction using Large Gaussian Reconstruction Transformers. Unlike existing 3D avatar methods that can only process fixed-length data, FastAvatar achieves incremental reconstruction, striking a good balance between modeling quality and inference speed based on available data volume, delivering high-quality models with sufficient data while providing viable reconstruction results with limited data.

Authors:

Yue Wu, Yufan Wu, Wen Li, Yuxi Lu, Kairui Feng, Xuanhong Chen, Tongji University, Shanghai Innovation Institute, Shanghai Jiao Tong University, AKool

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SinGS: Animatable Single-Image Human Gaussian Splats with Kinematic Priors

We propose SinGS, aiming to achieve high-quality and efficient animatable 3D avatar reconstruction. At the heart of SinGS are two key components: Kinematic Human Diffusion and GeometryPreserving 3D Gaussain Splatting. Extensive experiments demonstrate that SinGS enables lifelike, animatable human reconstructions, maintaining both high quality and inference efficiency (up to 70FPS).

Authors:

Yufan Wu, Xuanhong Chen, Wen Li, Shunran Jia, Hualiang Wei, Kairui Feng, Jialiang Chen, Yuhan Li, Ang He, Weimin Zhang, Bingbing Ni, Wenjun Zhang

Conference:

The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2025

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Learning Energy-Based Models by Cooperative Diffusion Recovery Likelihood

This research presents an innovative method for learning energy-based models using cooperative diffusion recovery likelihood. Our approach leverages the strengths of cooperative learning and diffusion processes to improve the training efficiency and effectiveness of energy-based models.

Authors:

Yaxuan Zhu, Jianwen Xie, Ying Nian Wu, Ruiqi Gao

Conference:

The Twelfth International Conference on Learning Representations (ICLR), 2024

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An Energy-Based Prior for Generative Saliency

In this paper, we introduce a novel energy-based prior for generative saliency models. Our approach enhances the interpretability and performance of saliency detection by integrating energy-based techniques, leading to more accurate and reliable results in various applications.

Authors:

Jing Zhang, Jianwen Xie, Nick Barnes, Ping Li

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IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023

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Latent Space Energy-Based Neural ODEs

This research introduces a novel framework for modeling continuous-time sequential data using Latent Space Energy-Based Neural Ordinary Differential Equations (ODEs). Our approach enhances the modeling of sequential data, offering new insights for machine learning tasks.

Authors:

Sheng Cheng, Deqian Kong, Jianwen Xie, Kookjin Lee, Ying Nian Wu, Yezhou Yang

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Transactions on Machine Learning Research (TMLR) 2025

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Molecule Design by Latent Prompt Transformer

We propose the Latent Prompt Transformer (LPT), a novel generative model for challenging problem of molecule design. Experiments demonstrate that LPT not only effectively discovers useful molecules across single-objective, multi-objective, and structure-constrained optimization tasks, but also exhibits strong sample efficiency.

Authors:

Deqian Kong, Yuhao Huang, Jianwen Xie, Edouardo Honig, Ming Xu, Shuanghong Xue, Pei Lin, Sanping Zhou, Sheng Zhong, Nanning Zheng, Ying Nian Wu

Conference:

The 38th Conference on Neural Information Processing Systems (NeurIPS 2024)

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Latent Energy-Based Odyssey: Black-Box Optimization via Expanded Exploration in the Energy-Based Latent Space

This paper introduces a novel black-box optimization method known as the Latent Energy-Based Odyssey. The approach focuses on expanded exploration within an energy-based latent space, enhancing the optimization process. By leveraging the latent space’s energy landscape, the method improves the efficiency and effectiveness of optimization tasks, making it a significant advancement in the field of machine learning and optimization.

Authors:

Peiyu Yu, Dinghuai Zhang, Hengzhi He, Xiaojian Ma, Ruiyao Miao, Yifan Lu, Yasi Zhang, Deqian Kong, Ruiqi Gao, Jianwen Xie, Guang Cheng, Ying Nian Wu

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CoopHash: Cooperative Learning of Multipurpose Descriptor and Contrastive Pair Generator via Variational MCMC Teaching for Supervised Image Hashing

CoopHash introduces a novel approach to supervised image hashing through cooperative learning. By leveraging Variational Markov Chain Monte Carlo (MCMC) teaching, it simultaneously optimizes a multipurpose descriptor and a contrastive pair generator. This innovative method enhances the efficiency and accuracy of image hashing, making it a significant advancement in the field.

Authors:

Khoa Doan, Jianwen Xie, Yaxuan Zhu, Yang Zhao, Ping Li

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Latent Plan Transformer for Trajectory Abstraction: Planning as Latent Space Inference

In this paper, we introduce the Latent Plan Transformer, a novel framework that treats planning as latent variable inference. Our approach combines the strengths of transformers and latent variable models to achieve robust and efficient planning in complex environments.

Authors:

Deqian Kong, Dehong Xu, Minglu Zhao, Bo Pang, Jianwen Xie, Andrew Lizarraga, Yuhao Huang, Sirui Xie, Ying Nian Wu

Conference:

The 38th Conference on Neural Information Processing Systems (NeurIPS 2024)

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Progressive Energy-Based Cooperative Learning for Multi-Domain Image-to-Image Translation

We propose a progressive energy-based cooperative learning framework for multi-domain image-to-image translation. This method addresses the challenges of domain adaptation and translation by progressively refining the learning process, resulting in superior performance across multiple domains.

Authors:

Weinan Song, Yaxuan Zhu, Lei He, Ying Nian Wu, Jianwen Xie

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ArXiv, 2024

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