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Ming Gui

12 accepted papers

2026

Adapting Self-Supervised Representations as a Latent Space for Efficient Generation

Ming Gui, Johannes Schusterbauer, Timy Phan, Felix Krause, Joshua M. Susskind, Miguel Ángel Bautista +1
ICLR 2026poster

We introduce Representation Tokenizer (RepTok), a generative modeling framework that represents an image using a single continuous latent token obtained from self-supervised vision transformers. Building on a pre-trained SSL encoder, we fine-tune only the semantic token embedding and pair it with a…

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2026

Denoising, Fast and Slow: Difficulty-Aware Adaptive Sampling for Image Generation

Johannes Schusterbauer, Ming Gui, Yusong Li, Pingchuan Ma, Felix Krause, Björn Ommer
CVPR 2026

Diffusion- and flow-based models usually allocate compute uniformly across space, updating all patches with the same timestep and number of function evaluations. While convenient, this ignores the heterogeneity of natural images: some regions are easy to denoise, whereas others benefit from more ref

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2026

Guiding Token-Sparse Diffusion Models

Felix Krause, Stefan Andreas Baumann, Johannes Schusterbauer, Olga Grebenkova, Ming Gui, Vincent Tao Hu +1
CVPR 2026

Diffusion models deliver high quality in image synthesis but remain expensive during training and inference. Recent works have leveraged the inherent redundancy in visual content to make training more affordable by training only on a subset of visual information. While these methods were successful

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2025

DepthFM: Fast Generative Monocular Depth Estimation with Flow Matching

Ming Gui, Johannes Schusterbauer, Ulrich Prestel, Pingchuan Ma, Dmytro Kotovenko, Olga Grebenkova +3
AAAI 2025technical

Current discriminative depth estimation methods often produce blurry artifacts, while generative approaches suffer from slow sampling due to curvatures in the noise-to-depth transport. Our method addresses these challenges by framing depth estimation as a direct transport between image and depth dis…

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2025

Diff2Flow: Training Flow Matching Models via Diffusion Model Alignment

Johannes Schusterbauer, Ming Gui, Frank Fundel, Björn Ommer
CVPR 2025poster

Diffusion models have revolutionized generative tasks through high-fidelity outputs, yet flow matching (FM) offers faster inference and empirical performance gains. However, current foundation FM models are computationally prohibitive for finetuning, while diffusion models like Stable Diffusion bene…

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2025

DisMo: Disentangled Motion Representations for Open-World Motion Transfer

Thomas Ressler-Antal, Frank Fundel, Malek Ben Alaya, Stefan Andreas Baumann, Felix Krause, Ming Gui +1
NeurIPS 2025spotlight

Recent advances in text-to-video (T2V) and image-to-video (I2V) models, have enabled the creation of visually compelling and dynamic videos from simple textual descriptions or initial frames. However, these models often fail to provide an explicit representation of motion separate from content, limi…

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2025

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models

Pingchuan Ma, Xiaopei Yang, Yusong Li, Ming Gui, Felix Krause, Johannes Schusterbauer +1
ICCV 2025poster

Explicitly disentangling style and content in vision models remains challenging due to their semantic overlap and the subjectivity of human perception. Existing methods propose separation through generative or discriminative objectives, but they still face the inherent ambiguity of disentangling int…

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2025

Stochastic Interpolants for Revealing Stylistic Flows across the History of Art

Pingchuan Ma, Ming Gui, Johannes Schusterbauer, Xiaopei Yang, Olga Grebenkova, Vincent Tao Hu +1
ICCV 2025accepted

Generative models have made rapid progress in content creation, particularly in synthesizing artworks and capturing stylistic variation. However, most methods operate at the level of individual images, limiting their ability to reveal broader stylistic trends and temporal transitions. We address thi…

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2025

TREAD: Token Routing for Efficient Architecture-agnostic Diffusion Training

Felix Krause, Timy Phan, Ming Gui, Stefan Andreas Baumann, Vincent Tao Hu, Björn Ommer
ICCV 2025poster

Diffusion models have emerged as the mainstream approach for visual generation. However, these models typically suffer from sample inefficiency and high training costs. Consequently, methods for efficient finetuning, inference and personalization were quickly adopted by the community. However, train…

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2024

FMBoost: Boosting Latent Diffusion with Flow Matching

Johannes S Fischer*, Ming Gui, Pingchuan Ma, Nick Stracke, Stefan Andreas Baumann, Vincent Tao Hu +1
ECCV 2024oral

"Visual synthesis has recently seen significant leaps in performance, largely due to breakthroughs in generative models. Diffusion models have been a key enabler, as they excel in image diversity. However, this comes at the cost of slow training and synthesis, which is only partially alleviated by l…

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2024

ZigMa: A DiT-style Zigzag Mamba Diffusion Model

Vincent Tao Hu*, Stefan A Baumann, Ming Gui, Olga Grebenkova, Pingchuan Ma, Johannes S Fischer +1
ECCV 2024poster

"The diffusion model has long been plagued by scalability and quadratic complexity issues, especially within transformer-based structures. In this study, we aim to leverage the long sequence modeling capability of a State-Space Model called Mamba to extend its applicability to visual data generation…

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