Jan Ackermann

TL;DR: We introduce FluidGen, which jointly generates fluid videos and per-frame velocity fields. Training on simulated and real fluid videos, followed by velocity-aware direct preference optimization, improves the physical plausibility of smoke, fire, and other fluid dynamics.

TL;DR: We introduce LoT Diffusion, which allocates finer tokens to regions that need detail and coarser tokens elsewhere. By adapting pretrained diffusion transformers to these multiresolution layouts, we reduce the token sequence while preserving full-resolution flow prediction, enabling adaptive image and video generation with favorable quality-efficiency tradeoffs.

TL;DR: We propose AsymFlow, a rank-asymmetric velocity parameterization that predicts noise in a low-rank subspace while preserving full-dimensional data prediction. This analytically recovers full-dimensional velocity without changing architecture or training, setting new state-of-the-art performance for pixel-space image generation and enabling effective finetuning from pretrained latent flow models.

TL;DR: We introduce a real-time, task-aware foveated imaging system that learns a policy to allocate scarce sensor bandwidth to the most informative regions while preserving low-resolution global context. This closes the perception-acquisition loop at capture time and delivers strong perception performance under strict pixel budgets in both simulation and real-world 200MP sensor experiments.

TL;DR: We introduce a geometry-consistency reward that enforces physically plausible motion in generated videos. By decomposing flow into rigid camera motion and independent object motion, we turn geometric coherence into an explicit optimization objective via reinforcement fine-tuning, substantially reducing temporal artifacts while preserving quality.

TL;DR: We propose a novel approach to video generation that decouples the control of time and camera pose. We introduce a new dataset and model that allows for the generation of videos with both temporal and spatial condition.

TL;DR: We explore how to adapt an existing 3DGS scene representation to new incremental changes. We propose a novel and efficient way to identify changed regions and then to locally optimize them. This not only produces more accurate scene updates but also enables new applications.

TL;DR: We introduce AIpparel, the first large-scale multimodal generative model designed specifically for digital garments. By extending LLaVA to incorporate a new garment modality, AIpparel enables the creation of sewing patterns from text, image, and garment inputs.

TL;DR: We explore the class of Linear and Sparse Transformers in a Chain-of-Thought (CoT) setting, finding that to match the performance of regular Transformers, their hidden dimensions must scale with the problem size.

TL;DR: We show that pretrained Mask-based segmentation models can predict anomalies without further tuning. Additionally, we introduce a metric for anomaly segmentation that favors models with confident predictions.