1 citations · 2 across the 14 of their papers we have counts for
19 papers
LongBanana: An Expert-Verified Benchmark for Long-Context Multi-Reference Image Synthesis
Haoxiang Cao, Yuxuan Zhang, Penghui Du +16
We introduce LongBanana, to our knowledge the first expert-designed and expert-verified benchmark for long-context multi-reference image synthesis in the age of agent-driven image…
EmoReward: Self-Evolving Reward Calibration for Emotional Video Generation RL
Mingzhe Zheng, Yue Wu, Jing Tan +19
Group Relative Policy Optimization (GRPO) has advanced generative model post-training in verifiable domains such as code and mathematics, but evaluating video quality, especially e…
Do Agentic Policies Need to Look at Every Step? Geometric Abstraction for Reasoning and Action
Yihang Liu, Yiming Chen, Mingzhe Zheng
Recent multimodal language models can reason directly about robot scenes, but it is unclear whether they need to repeatedly reinterpret raw visual observations at every step of an…
An Empirical Study of Training Pixel-Space Text-to-Image Diffusion Models
Dengyang Jiang, Ruoyi Du, Zhennan Chen +10
This paper investigates an increasingly important topic in generative modeling: pixel-space diffusion models. Although numerous studies have explored this topic, most focus on smal…
Twins: Learn to Predict Unified Representations with Focal Loss
Kaixiong Gong, Xin Cai, Bin Lin +9
Unified multimodal models seek a shared visual token space that supports both multimodal understanding and image generation. Discrete methods unify the interface via a shared codeb…
ICDepth: Taming Video Diffusion Models for Video Depth Estimation via In-Context Conditioning
Xuanhua He, Jiaxin Xie, Mingzhe Zheng +1
Monocular video depth estimation requires temporal consistency, geometric accuracy, and generalization across diverse scenarios, yet existing methods struggle to achieve all three…
D-OPSD: On-Policy Self-Distillation for Continuously Tuning Step-Distilled Diffusion Models
Dengyang Jiang, Xin Jin, Dongyang Liu +9
The landscape of high-performance image generation models is currently shifting from the inefficient multi-step ones to the efficient few-step counterparts (e.g, Z-Image-Turbo and…
FastVMT: Eliminating Redundancy in Video Motion Transfer
Yue Ma, Zhikai Wang, Tianhao Ren +9
Video motion transfer aims to synthesize videos by generating visual content according to a text prompt while transferring the motion pattern observed in a reference video. Recent…
Group Editing: Edit Multiple Images in One Go
Yue Ma, Xinyu Wang, Qianli Ma +9
In this paper, we tackle the problem of performing consistent and unified modifications across a set of related images. This task is particularly challenging because these images m…
Manifold-Aware Exploration for Reinforcement Learning in Video Generation
Mingzhe Zheng, Weijie Kong, Yue Wu +9
Group Relative Policy Optimization (GRPO) methods for video generation like FlowGRPO remain far less reliable than their counterparts for language models and images. This gap arise…
iFSQ: Improving FSQ for Image Generation with 1 Line of Code
Bin Lin, Zongjian Li, Yuwei Niu +9
The field of image generation is currently bifurcated into autoregressive (AR) models operating on discrete tokens and diffusion models utilizing continuous latents. This divide, r…
CML-Bench: A Framework for Evaluating and Enhancing LLM-Powered Movie Scripts Generation
Mingzhe Zheng, Dingjie Song, Guanyu Zhou +7
Large Language Models (LLMs) have demonstrated remarkable proficiency in generating highly structured texts. However, while exhibiting a high degree of structural organization, mov…
Follow-Your-Shape: Shape-Aware Image Editing via Trajectory-Guided Region Control
Zeqian Long, Mingzhe Zheng, Kunyu Feng +6
While recent flow-based image editing models demonstrate general-purpose capabilities across diverse tasks, they often struggle to specialize in challenging scenarios -- particular…
Controllable Video Generation: A Survey
Yue Ma, Kunyu Feng, Zhongyuan Hu +19
With the rapid development of AI-generated content (AIGC), video generation has emerged as one of its most dynamic and impactful subfields. In particular, the advancement of video…
Model Reveals What to Cache: Profiling-Based Feature Reuse for Video Diffusion Models
Xuran Ma, Yexin Liu, Yaofu Liu +5
Recent advances in diffusion models have demonstrated remarkable capabilities in video generation. However, the computational intensity remains a significant challenge for practica…
VideoGen-of-Thought: Step-by-step generating multi-shot video with minimal manual intervention
Mingzhe Zheng, Yongqi Xu, Haojian Huang +8
Current video generation models excel at short clips but fail to produce cohesive multi-shot narratives due to disjointed visual dynamics and fractured storylines. Existing solutio…
VideoGen-of-Thought: Step-by-step generating multi-shot video with minimal manual intervention
Mingzhe Zheng, Yongqi Xu, Haojian Huang +8
Current video generation models excel at short clips but fail to produce cohesive multi-shot narratives due to disjointed visual dynamics and fractured storylines. Existing solutio…
DreamDance: Animating Human Images by Enriching 3D Geometry Cues from 2D Poses
Yatian Pang, Bin Zhu, Bin Lin +5
In this work, we present DreamDance, a novel method for animating human images using only skeleton pose sequences as conditional inputs. Existing approaches struggle with generatin…
FineCLIPER: Multi-modal Fine-grained CLIP for Dynamic Facial Expression Recognition with AdaptERs
Haodong Chen, Haojian Huang, Junhao Dong +2
Dynamic Facial Expression Recognition (DFER) is crucial for understanding human behavior. However, current methods exhibit limited performance mainly due to the scarcity of high-qu…