Diffusion Guidance

Follow this topic Ask about this topic

Momentum

13 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 78

All topics

$h$ -control: Training-Free Camera Control via Block-Conditional Gibbs Refinement
May 12, 2026Yuzhu Wang, Xi Ye, Duo Su +2Markov Chain Monte Carlo Camera-Controlled Video Generation
Department of Computer Science and Technology, Tsinghua University · South China University of Technology
Regret Analysis of Guided Diffusion for Black-Box Optimization over Structured Inputs
May 11, 2026Masaki Adachi, Anita Yang, Yakun Wang +1Bayesian Optimization Black-Box Optimization
Toyota Motor Corporation · Lattice Lab · University of Bristol
Primal-Dual Guided Decoding for Constrained Discrete Diffusion
May 10, 2026Federico Tomasi, Dmitrii Moor, Alice Wang +1Primal-Dual Optimization Constrained Generative Modeling
Spotify
ConFixGS: Learning to Fix Feedforward 3D Gaussian Splatting with Confidence-Aware Diffusion Priors in Driving Scenes
May 10, 2026Rui Song, Tianhui Cai, Markus Gross +53D Gaussian Splatting Diffusion Guidance
University of California, Los Angeles · Technical University of Munich · University of Cambridge
Towards Highly-Constrained Human Motion Generation with Retrieval-Guided Diffusion Noise Optimization
May 8, 2026Hanchao Liu, Fang-Lue Zhang, Shining Zhang +2Constrained Generative Modeling Retrieval-Augmented Generation
Tsinghua University · University of New South Wales
On the Robustness of Distribution Support under Diffusion Guidance
May 8, 2026Ruijia Cao, Yuchen Wu, Nisha ChandramoorthyDiffusion Guidance Diffusion Sampling
Center for Applied Mathematics, Cornell University · School of Operations Research and Information Engineering, Cornell University · Department of Statistics, The University of Chicago
Diffusion-Based Posterior Sampling: A Feynman-Kac Analysis of Bias and Stability
May 7, 2026Matias G. Delgadino, Sebastien Motsch, Advait Parulekar +2Stochastic Differential Equations Bayesian Inverse Problems
Department of Mathematics, UT Austin. · School of Mathematical and Statistical Sciences, Arizona State University. · Department of Electrical and Computer Engineering, UT Austin.
Refining Compositional Diffusion for Reliable Long-Horizon Planning
May 4, 2026Kyowoon Lee, Yunhao Luo, Anh Tong +1Trajectory Generation Diffusion-Based Planning
KAIST · University of Michigan · Korea University +1
Decoupled Guidance Diffusion for Adaptive Offline Safe Reinforcement Learning
May 4, 2026Rufeng Chen, Zhaofan Zhang, Zhejiang Yang +2Diffusion-Based Planning Diffusion Guidance
1The Hong Kong University of Science and Technology (Guangzhou) · 2Jilin University
KinetiDiff: Docking-Guided Diffusion for De Novo ACVR1 Inhibitor Design in Fibrodysplasia Ossificans Progressiva
Apr 17, 2026Aaryan PatelMolecular Optimization Diffusion Guidance
Saugus High School, Santa Clarita, CA
Adaptive Weighted h-Transform Sampling for Coarse-Guided Visual Generation
Mar 12, 2026Yanghao Wang, Ziqi Jiang, Zhen Wang +1Diffusion Model Guidance Diffusion Guidance
The Hong Kong University of Science and Technology
Momentum Guidance: Plug-and-Play Guidance for Flow Models
Feb 23, 2026Runlong Liao, Jian Yu, Baiyu Su +3Momentum Methods Diffusion Guidance
The University of Texas at Austin, Austin, TX 78712, USA
EMAG: Self-Rectifying Diffusion Sampling with Exponential Moving Average Guidance
Dec 19, 2025Ankit Yadav, Ta Duc Huy, Lingqiao LiuDiffusion Model Guidance Classifier-Free Guidance
Australian Institute for Machine Learning, Adelaide University, Australia
Jacobian-Aware Posterior Sampling for Inverse Problems
Nov 23, 2025Liav Hen, Tom Tirer, Raja Giryes +1Bayesian Inverse Problems Diffusion Guidance
Tel Aviv University, Israel · Bar-Ilan University, Israel · University of Cambridge, UK
RFG: Self-Improving Diffusion Large Language Models with Reward-Free Guidance
Sep 29, 2025Tianlang Chen, Minkai Xu, Jure Leskovec +1Diffusion Guidance Diffusion Language Model Inference
Stanford University
Multi-Embodiment Robotic Retargeting via Guided Diffusion Model
May 27, 2025Zhefeng Cao, Ben Liu, Shunpeng Yang +3Diffusion Guidance Cross-Embodiment Robot Learning
Southern University of Science and Technology, Shenzhen, China · The Hong Kong University of Science and Technology, HongKong, China · LimX Dynamics, Shenzhen, China. +1
ContrastiveCFG: Guiding Diffusion Sampling by Contrasting Positive and Negative Concepts
Nov 26, 2024Jinho Chang, Changsun Lee, Hyungjin Chung +1Diffusion Model Guidance Classifier-Free Guidance
Graduate School of Artificial Intelligence, Korea Advanced Institute of Science and Technology, Seoul, South Korea · EverEx, Seoul, South Korea
Denoising as Projection: Constrained Optimization with Gradient-Guided Diffusion
Date pendingRunyu Zhang, Jiawei Zhang, Gioele Zardini +2Riemannian Optimization Diffusion Guidance
MIT · University of Wisconsin–Madison

May 12, 2026, cs.CVJ/K move · Enter open · S save

$h$ -control: Training-Free Camera Control via Block-Conditional Gibbs Refinement

Yuzhu Wang, Xi Ye, Duo Su, Yangyang Xu+1

Department of Computer Science and Technology, Tsinghua University · South China University of Technology

Markov Chain Monte Carlo Camera-Controlled Video Generation Diffusion Guidance Flow-Based Generative Modeling Controllable Video Generation

Open paper AskPDF arXiv

Training-free camera control for pretrained flow-matching video generators is a partial-observation inverse problem: a depth-warped guidance video supplies noisy evidence on a subset of latent sites, which the sampler must reconcile with the pretrained prior. Existing methods struggle to balance the trade-off between trajectory adherence and visual quality and the heuristic guidance-strength tuning lacks robustness. We propose \textbf{

h

-control}, which resolves this dilemma through a structural change to the sampler: each outer hard-replacement guidance step is augmented with an inner-loop \emph{block-conditional pseudo-Gibbs refinement} on the unobserved complement at the same noise level, with provable convergence to the partial-observation conditional data law. To accelerate convergence on high-dimensional video latents, we exploit their conditional locality, partitioning the unobserved complement into 3D patches, each tracked by a custom mixing indicator that adaptively freezes converged patches. On RealEstate10K and DAVIS, \textbf{

h

-control} attains the best FVD against all seven training-free and training-based competitors, outperforming every training-free baseline on every reported metric.

Momentum

Related topics

Latest papers 78