activity
20162026
most citedAdding Conditional Control to Text-to-Image Diffusion Models

222 citations · 543 across the 56 of their papers we have counts for

collaborators
Showing 2025Show all

16 papers · 1 filter

cs.CV2025

LouvreSAE: Sparse Autoencoders for Interpretable and Controllable Style Transfer

Raina Panda, Daniel Fein, Arpita Singhal +3

Artistic style transfer in generative models remains a significant challenge, as existing methods often introduce style only via model fine-tuning, additional adapters, or prompt e…

cs.CV2025

TinyHistory: Lightweight Video History Embeddings via Two-Stage Context Learning

Lvmin Zhang, Shengqu Cai, Muyang Li +6

History context is central to autoregressive video generation, driving consistency and storytelling for both commercial models and personal use cases. For example, personal users,…

cs.CV2025

StreamDiffusionV2: A Streaming System for Dynamic and Interactive Video Generation

Tianrui Feng, Zhi Li, Shuo Yang +11

Generative models are reshaping the live-streaming industry by redefining how content is created, styled, and delivered. Previous image-based streaming diffusion models have powere…

cs.MA2025

Hollywood Town: Long-Video Generation via Cross-Modal Multi-Agent Orchestration

Zheng Wei, Mingchen Li, Zeqian Zhang +6

Recent advancements in multi-agent systems have demonstrated significant potential for enhancing creative task performance, such as long video generation. This study introduces thr…

cs.RO2025

Kinematic Kitbashing

Minghao Guo, Victor Zordan, Sheldon Andrews +3

We introduce Kinematic Kitbashing, an optimization framework that synthesizes articulated 3D objects by assembling reusable parts conditioned on an abstract kinematic graph. Given…

cs.CV2025

Taming Flow-based I2V Models for Creative Video Editing

Xianghao Kong, Hansheng Chen, Yuwei Guo +4

Although image editing techniques have advanced significantly, video editing, which aims to manipulate videos according to user intent, remains an emerging challenge. Most existing…

cs.HC2025

CineVision: An Interactive Pre-visualization Storyboard System for Director-Cinematographer Collaboration

Zheng Wei, Hongtao Wu, Lvmin Zhang +6

Effective communication between directors and cinematographers is fundamental in film production, yet traditional approaches relying on visual references and hand-drawn storyboards…

cs.GR2025★ 1 cited

Mixture of Contexts for Long Video Generation

Shengqu Cai, Ceyuan Yang, Lvmin Zhang +10

Long video generation is fundamentally a long context memory problem: models must retain and retrieve salient events across a long range without collapsing or drifting. However, sc…

cs.CV2025

Captain Cinema: Towards Short Movie Generation

Junfei Xiao, Ceyuan Yang, Lvmin Zhang +7

We present Captain Cinema, a generation framework for short movie generation. Given a detailed textual description of a movie storyline, our approach firstly generates a sequence o…

cs.GR2025

Uncovering Conceptual Blindspots in Generative Image Models Using Sparse Autoencoders

Matyas Bohacek, Thomas Fel, Maneesh Agrawala +1

Despite their impressive performance, generative image models trained on large-scale datasets frequently fail to produce images with seemingly simple concepts -- e.g., human hands…

cs.CV2025

Radial Attention: Sparse Attention with Energy Decay for Long Video Generation

Xingyang Li, Muyang Li, Tianle Cai +11

Recent advances in diffusion models have enabled high-quality video generation, but the additional temporal dimension significantly increases computational costs, making training a…

cs.CV2025

Frame Context Packing and Drift Prevention in Next-Frame-Prediction Video Diffusion Models

Lvmin Zhang, Shengqu Cai, Muyang Li +2

We present a neural network structure, FramePack, to train next-frame (or next-frame-section) prediction models for video generation. FramePack compresses input frame contexts with…

cs.CV2025

Efficient Autoregressive Shape Generation via Octree-Based Adaptive Tokenization

Kangle Deng, Hsueh-Ti Derek Liu, Yiheng Zhu +7

Many 3D generative models rely on variational autoencoders (VAEs) to learn compact shape representations. However, existing methods encode all shapes into a fixed-size token, disre…

cs.CV2025★ 1 cited

Cube: A Roblox View of 3D Intelligence

Foundation AI Team, Kiran Bhat, Nishchaie Khanna +44

Foundation models trained on vast amounts of data have demonstrated remarkable reasoning and generation capabilities in the domains of text, images, audio and video. Our goal at Ro…

cs.GR2025

MoVer: Motion Verification for Motion Graphics Animations

Jiaju Ma, Maneesh Agrawala

While large vision-language models can generate motion graphics animations from text prompts, they regularly fail to include all spatio-temporal properties described in the prompt.…

cs.CV2025★ 2 cited

Instance Segmentation of Scene Sketches Using Natural Image Priors

Mia Tang, Yael Vinker, Chuan Yan +2

Sketch segmentation involves grouping pixels within a sketch that belong to the same object or instance. It serves as a valuable tool for sketch editing tasks, such as moving, scal…