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cs.CV2026

Pictura: Perspective-View Self-Play at Scale for Driving

Yuan Yin, Elias Ramzi, Marc Lafon +8

The paper presents Pictura, a GPU‑accelerated multi‑agent driving simulator that renders each vehicle's egocentric camera view, enabling large‑scale self‑play training of driving p…

cs.CV2026

Representation Distribution Matching for One-Step Visual Generation

Lan Feng, Wuyang Li, Eloi Zablocki +2

We elucidate the design space of Representation Distribution Matching (RDM), our name for the paradigm that trains a one-step image generator by matching generated and reference fe…

cs.CV2026

PPT: Pretraining with Pseudo-Labeled Trajectories for Motion Forecasting

Yihong Xu, Yuan Yin, Éloi Zablocki +3

Accurately predicting how agents move in dynamic scenes is essential for safe autonomous driving. State-of-the-art motion forecasting models rely on datasets with manually annotate…

cs.CV2026

GIFT: A Framework Towards Global Interpretable Faithful Textual Explanations of Vision Classifiers

Éloi Zablocki, Valentin Gerard, Amaia Cardiel +3

Understanding the decision processes of deep vision models is essential for their safe and trustworthy deployment in real-world settings. Existing explainability approaches, such a…

cs.CV2026

RAP: 3D Rasterization Augmented End-to-End Planning

Lan Feng, Yang Gao, Eloi Zablocki +5

Imitation learning for end-to-end driving trains policies only on expert demonstrations. Once deployed in a closed loop, such policies lack recovery data: small mistakes cannot be…

cs.CV2026

MAD: Motion Appearance Decoupling for efficient Driving World Models

Ahmad Rahimi, Valentin Gerard, Eloi Zablocki +2

Recent video diffusion models generate photorealistic, temporally coherent videos, yet they fall short as reliable world models for autonomous driving, where structured motion and…