1 citations · 4 across the 9 of their papers we have counts for
9 papers
BlobGEN-Vid: Compositional Text-to-Video Generation with Blob Video Representations
Weixi Feng, Chao Liu, Sifei Liu +3
Existing video generation models struggle to follow complex text prompts and synthesize multiple objects, raising the need for additional grounding input for improved controllabili…
T2V-Turbo-v2: Enhancing Video Generation Model Post-Training through Data, Reward, and Conditional Guidance Design
Jiachen Li, Qian Long, Jian Zheng +4
In this paper, we focus on enhancing a diffusion-based text-to-video (T2V) model during the post-training phase by distilling a highly capable consistency model from a pretrained T…
MMWorld: Towards Multi-discipline Multi-faceted World Model Evaluation in Videos
Xuehai He, Weixi Feng, Kaizhi Zheng +11
Multimodal Language Language Models (MLLMs) demonstrate the emerging abilities of "world models" -- interpreting and reasoning about complex real-world dynamics. To assess these ab…
Global Human-guided Counterfactual Explanations for Molecular Properties via Reinforcement Learning
Danqing Wang, Antonis Antoniades, Kha-Dinh Luong +6
Counterfactual explanations of Graph Neural Networks (GNNs) offer a powerful way to understand data that can naturally be represented by a graph structure. Furthermore, in many dom…
TC-Bench: Benchmarking Temporal Compositionality in Text-to-Video and Image-to-Video Generation
Weixi Feng, Jiachen Li, Michael Saxon +3
Video generation has many unique challenges beyond those of image generation. The temporal dimension introduces extensive possible variations across frames, over which consistency…
BPO: Staying Close to the Behavior LLM Creates Better Online LLM Alignment
Wenda Xu, Jiachen Li, William Yang Wang +1
Direct alignment from preferences (DAP) has emerged as a promising paradigm for aligning large language models (LLMs) to human desiderata from pre-collected, offline preference dat…
T2V-Turbo: Breaking the Quality Bottleneck of Video Consistency Model with Mixed Reward Feedback
Jiachen Li, Weixi Feng, Tsu-Jui Fu +4
Diffusion-based text-to-video (T2V) models have achieved significant success but continue to be hampered by the slow sampling speed of their iterative sampling processes. To addres…
Reward Guided Latent Consistency Distillation
Jiachen Li, Weixi Feng, Wenhu Chen +1
Latent Consistency Distillation (LCD) has emerged as a promising paradigm for efficient text-to-image synthesis. By distilling a latent consistency model (LCM) from a pre-trained t…
Mastering Robot Manipulation with Multimodal Prompts through Pretraining and Multi-task Fine-tuning
Jiachen Li, Qiaozi Gao, Michael Johnston +6
Prompt-based learning has been demonstrated as a compelling paradigm contributing to large language models' tremendous success (LLMs). Inspired by their success in language tasks,…