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World Model Self-Distillation: Training World Models to Solve General Tasks
Sebastian Stapf, Pablo Acuaviva Huertos, Aram Davtyan +1
Pretrained video generators are promising visual world models that exhibit emergent task-solving abilities; however, their reliance on detailed textual descriptions limits their di…
Communication-Inspired Tokenization for Structured Image Representations
Aram Davtyan, Yusuf Sahin, Yasaman Haghighi +4
Discrete image tokenizers have emerged as a key component of modern vision and multimodal systems, providing a sequential interface for transformer-based architectures. However, mo…
Rethinking Visual Intelligence: Insights from Video Pretraining
Pablo Acuaviva, Aram Davtyan, Mariam Hassan +4
Large language models (LLMs) have demonstrated that large-scale pretraining enables systems to adapt rapidly to new problems with little supervision in the language domain. This su…
From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models
Pablo Acuaviva, Aram Davtyan, Mariam Hassan +4
Video Diffusion Models (VDMs) have emerged as powerful generative tools, capable of synthesizing high-quality spatiotemporal content. Yet, their potential goes far beyond mere vide…
GEM: A Generalizable Ego-Vision Multimodal World Model for Fine-Grained Ego-Motion, Object Dynamics, and Scene Composition Control
Mariam Hassan, Sebastian Stapf, Ahmad Rahimi +17
We present GEM, a Generalizable Ego-vision Multimodal world model that predicts future frames using a reference frame, sparse features, human poses, and ego-trajectories. Hence, ou…
CAGE: Unsupervised Visual Composition and Animation for Controllable Video Generation
Aram Davtyan, Sepehr Sameni, Björn Ommer +1
The field of video generation has expanded significantly in recent years, with controllable and compositional video generation garnering considerable interest. Most methods rely on…