8 citations · 10 across the 11 of their papers we have counts for
12 papers · 1 filter
Think in Strokes, Not Pixels: Process-Driven Image Generation via Interleaved Reasoning
Lei Zhang, Junjiao Tian, Zhipeng Fan +9
Humans paint images incrementally: they plan a global layout, sketch a coarse draft, inspect, and refine details, and most importantly, each step is grounded in the evolving visual…
UniT: Unified Multimodal Chain-of-Thought Test-time Scaling
Leon Liangyu Chen, Haoyu Ma, Zhipeng Fan +11
Unified models can handle both multimodal understanding and generation within a single architecture, yet they typically operate in a single pass without iteratively refining their…
Non-Markov Multi-Round Conversational Image Generation with History-Conditioned MLLMs
Haochen Zhang, Animesh Sinha, Felix Juefei-Xu +8
Conversational image generation requires a model to follow user instructions across multiple rounds of interaction, grounded in interleaved text and images that accumulate as chat…
Exploring MLLM-Diffusion Information Transfer with MetaCanvas
Han Lin, Xichen Pan, Ziqi Huang +10
Multimodal learning has rapidly advanced visual understanding, largely via multimodal large language models (MLLMs) that use powerful LLMs as cognitive cores. In visual generation,…
Improving Chain-of-Thought Efficiency for Autoregressive Image Generation
Zeqi Gu, Markos Georgopoulos, Xiaoliang Dai +10
Autoregressive multimodal large language models have recently gained popularity for image generation, driven by advances in foundation models. To enhance alignment and detail, newe…
Populate-A-Scene: Affordance-Aware Human Video Generation
Mengyi Shan, Zecheng He, Haoyu Ma +4
Can a video generation model be repurposed as an interactive world simulator? We explore the affordance perception potential of text-to-video models by teaching them to predict hum…
Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models
Xu Ma, Peize Sun, Haoyu Ma +22
Autoregressive (AR) models, long dominant in language generation, are increasingly applied to image synthesis but are often considered less competitive than Diffusion-based models.…
MoCha: Towards Movie-Grade Talking Character Synthesis
Cong Wei, Bo Sun, Haoyu Ma +10
Recent advancements in video generation have achieved impressive motion realism, yet they often overlook character-driven storytelling, a crucial task for automated film, animation…
Movie Weaver: Tuning-Free Multi-Concept Video Personalization with Anchored Prompts
Feng Liang, Haoyu Ma, Zecheng He +10
Video personalization, which generates customized videos using reference images, has gained significant attention. However, prior methods typically focus on single-concept personal…
Llama Learns to Direct: DirectorLLM for Human-Centric Video Generation
Kunpeng Song, Tingbo Hou, Zecheng He +12
In this paper, we introduce DirectorLLM, a novel video generation model that employs a large language model (LLM) to orchestrate human poses within videos. As foundational text-to-…
Movie Gen: A Cast of Media Foundation Models
Adam Polyak, Amit Zohar, Andrew Brown +85
We present Movie Gen, a cast of foundation models that generates high-quality, 1080p HD videos with different aspect ratios and synchronized audio. We also show additional capabili…
Imagine yourself: Tuning-Free Personalized Image Generation
Zecheng He, Bo Sun, Felix Juefei-Xu +14
Diffusion models have demonstrated remarkable efficacy across various image-to-image tasks. In this research, we introduce Imagine yourself, a state-of-the-art model designed for p…