activity
20182025
most citedEmu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack

30 citations · 46 across the 8 of their papers we have counts for

collaborators
Showing cs.CVShow all

8 papers · 1 filter

cs.CV2025

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.…

cs.CV20241 cited

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-…

cs.CV20248 cited

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…

cs.CV2024

Pixel-Space Post-Training of Latent Diffusion Models

Christina Zhang, Simran Motwani, Matthew Yu +6

Latent diffusion models (LDMs) have made significant advancements in the field of image generation in recent years. One major advantage of LDMs is their ability to operate in a com…

cs.CV20243 cited

Cache Me if You Can: Accelerating Diffusion Models through Block Caching

Felix Wimbauer, Bichen Wu, Edgar Schoenfeld +11

Diffusion models have recently revolutionized the field of image synthesis due to their ability to generate photorealistic images. However, one of the major drawbacks of diffusion…

cs.CV2023

ControlRoom3D: Room Generation using Semantic Proxy Rooms

Jonas Schult, Sam Tsai, Lukas Höllein +11

Manually creating 3D environments for AR/VR applications is a complex process requiring expert knowledge in 3D modeling software. Pioneering works facilitate this process by genera…