5 citations · 14 across the 18 of their papers we have counts for
8 papers · 1 filter
LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models
Zhengyi Wang, Jonathan Lorraine, Yikai Wang +4
This work explores expanding the capabilities of large language models (LLMs) pretrained on text to generate 3D meshes within a unified model. This offers key advantages of (1) lev…
Toward Guidance-Free AR Visual Generation via Condition Contrastive Alignment
Huayu Chen, Hang Su, Peize Sun +1
Classifier-Free Guidance (CFG) is a critical technique for enhancing the sample quality of visual generative models. However, in autoregressive (AR) multi-modal generation, CFG int…
RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation
Songming Liu, Lingxuan Wu, Bangguo Li +6
Bimanual manipulation is essential in robotics, yet developing foundation models is extremely challenging due to the inherent complexity of coordinating two robot arms (leading to…
Aligning Diffusion Behaviors with Q-functions for Efficient Continuous Control
Huayu Chen, Kaiwen Zheng, Hang Su +1
Drawing upon recent advances in language model alignment, we formulate offline Reinforcement Learning as a two-stage optimization problem: First pretraining expressive generative p…
MicroDreamer: Efficient 3D Generation in 20 Seconds by Score-based Iterative Reconstruction
Luxi Chen, Zhengyi Wang, Zihan Zhou +4
Optimization-based approaches, such as score distillation sampling (SDS), show promise in zero-shot 3D generation but suffer from low efficiency, primarily due to the high number o…
CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Model
Zhengyi Wang, Yikai Wang, Yifei Chen +6
Feed-forward 3D generative models like the Large Reconstruction Model (LRM) have demonstrated exceptional generation speed. However, the transformer-based methods do not leverage t…
Noise Contrastive Alignment of Language Models with Explicit Rewards
Huayu Chen, Guande He, Lifan Yuan +3
User intentions are typically formalized as evaluation rewards to be maximized when fine-tuning language models (LMs). Existing alignment methods, such as Direct Preference Optimiz…
Your Diffusion Model is Secretly a Certifiably Robust Classifier
Huanran Chen, Yinpeng Dong, Shitong Shao +4
Generative learning, recognized for its effective modeling of data distributions, offers inherent advantages in handling out-of-distribution instances, especially for enhancing rob…