1 citations · 2 across the 13 of their papers we have counts for
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DualHeads: Reward-tilted few-step diffusion made simple
Yuanzhi Zhu, Eleftherios Tsonis, Simo Alami Chehboune +3
Distilled diffusion models generate high-quality images in a few sampling steps, but aligning them with task requirements and human preferences is difficult. Methods developed for…
What Images Cannot Say: Language-Guided Olfactory Representation Learning
Eleftherios Tsonis, Xi Wang, Vicky Kalogeiton
Images tell us what a scene looks like, but rarely what it would feel like to be there. While recent datasets pair visual scenes with electronic-nose measurements, aligning smell s…
Chain of Evidence: Pixel-Level Visual Attribution for Iterative Retrieval-Augmented Generation
Peiyang Liu, Ziqiang Cui, Xi Wang +2
Iterative Retrieval-Augmented Generation (iRAG) has emerged as a powerful paradigm for answering complex multi-hop questions by progressively retrieving and reasoning over external…
SF20K Competition 2025: Summary and findings
Ridouane Ghermi, Xi Wang, Vicky Kalogeiton +1
This report presents the results and findings of the first edition of the Short-Films 20K (SF20K) Competition, held in conjunction with the SLoMO Workshop at ICCV 2025. The competi…
Diffusion Reinforcement Learning via Centered Reward Distillation
Yuanzhi Zhu, Xi Wang, Stéphane Lathuilière +1
Diffusion and flow models achieve State-Of-The-Art (SOTA) generative performance, yet many practically important behaviors such as fine-grained prompt fidelity, compositional corre…
VIGOR: VIdeo Geometry-Oriented Reward for Temporal Generative Alignment
Tengjiao Yin, Jinglei Shi, Heng Guo +1
Video diffusion models lack explicit geometric supervision during training, leading to inconsistency artifacts such as object deformation, spatial drift, and depth violations in ge…
Soft-Di[M]O: Improving One-Step Discrete Image Generation with Soft Embeddings
Yuanzhi Zhu, Xi Wang, Stéphane Lathuilière +1
One-step generators distilled from Masked Diffusion Models (MDMs) compress multiple sampling steps into a single forward pass, enabling efficient text and image synthesis. However,…
FakeParts: a New Family of AI-Generated DeepFakes
Ziyi Liu, Firas Gabetni, Awais Hussain Sani +5
We introduce FakeParts, a new class of deepfakes characterized by subtle, localized manipulations to specific spatial regions or temporal segments of otherwise authentic videos. Un…
DiO: Distilling Masked Diffusion Models into One-step Generator
Yuanzhi Zhu, Xi Wang, Stéphane Lathuilière +1
Masked Diffusion Models (MDMs) have emerged as a powerful generative modeling technique. Despite their remarkable results, they typically suffer from slow inference with several st…
LEAD: Latent Realignment for Human Motion Diffusion
Nefeli Andreou, Xi Wang, Victoria Fernández Abrevaya +3
Our goal is to generate realistic human motion from natural language. Modern methods often face a trade-off between model expressiveness and text-to-motion alignment. Some align te…
E.T. the Exceptional Trajectories: Text-to-camera-trajectory generation with character awareness
Robin Courant, Nicolas Dufour, Xi Wang +2
Stories and emotions in movies emerge through the effect of well-thought-out directing decisions, in particular camera placement and movement over time. Crafting compelling camera…
Long Story Short: Story-level Video Understanding from 20K Short Films
Ridouane Ghermi, Xi Wang, Vicky Kalogeiton +1
Recent developments in vision-language models have significantly advanced video understanding. Existing datasets and tasks, however, have notable limitations. Most datasets are con…
Analysis of Classifier-Free Guidance Weight Schedulers
Xi Wang, Nicolas Dufour, Nefeli Andreou +4
Classifier-Free Guidance (CFG) enhances the quality and condition adherence of text-to-image diffusion models. It operates by combining the conditional and unconditional prediction…