8 citations · 20 across the 15 of their papers we have counts for
16 papers · 1 filter
Omni-Diffusion-Distill: Few-Step Distillation of Unified Multimodal Diffusion Large Language Models
Hong Huang, Chenhongyi Yang, Junzhe Sun +3
Unified multimodal diffusion large language models (dLLMs) offer a single architecture for both image generation and multimodal understanding, but their iterative decoding requires…
ViTok-v2: Scaling Native Resolution Auto-Encoders to 5 Billion Parameters
Philippe Hansen-Estruch, Jiahui Chen, Vivek Ramanujan +9
Vision Transformer (ViT) autoencoders have emerged as compelling tokenizers for images, offering improved reconstruction over convolutional tokenizers. However, existing ViT tokeni…
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…
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…
Gen2Det: Generate to Detect
Saksham Suri, Fanyi Xiao, Animesh Sinha +4
Recently diffusion models have shown improvement in synthetic image quality as well as better control in generation. We motivate and present Gen2Det, a simple modular pipeline to c…
GenTron: Diffusion Transformers for Image and Video Generation
Shoufa Chen, Mengmeng Xu, Jiawei Ren +7
In this study, we explore Transformer-based diffusion models for image and video generation. Despite the dominance of Transformer architectures in various fields due to their flexi…
Context Diffusion: In-Context Aware Image Generation
Ivona Najdenkoska, Animesh Sinha, Abhimanyu Dubey +3
We propose Context Diffusion, a diffusion-based framework that enables image generation models to learn from visual examples presented in context. Recent work tackles such in-conte…
Text-to-Sticker: Style Tailoring Latent Diffusion Models for Human Expression
Animesh Sinha, Bo Sun, Anmol Kalia +14
We introduce Style Tailoring, a recipe to finetune Latent Diffusion Models (LDMs) in a distinct domain with high visual quality, prompt alignment and scene diversity. We choose sti…
FaD-VLP: Fashion Vision-and-Language Pre-training towards Unified Retrieval and Captioning
Suvir Mirchandani, Licheng Yu, Mengjiao Wang +4
Multimodal tasks in the fashion domain have significant potential for e-commerce, but involve challenging vision-and-language learning problems - e.g., retrieving a fashion item gi…
CommerceMM: Large-Scale Commerce MultiModal Representation Learning with Omni Retrieval
Licheng Yu, Jun Chen, Animesh Sinha +4
We introduce CommerceMM - a multimodal model capable of providing a diverse and granular understanding of commerce topics associated to the given piece of content (image, text, ima…
Large-Scale Attribute-Object Compositions
Filip Radenovic, Animesh Sinha, Albert Gordo +2
We study the problem of learning how to predict attribute-object compositions from images, and its generalization to unseen compositions missing from the training data. To the best…