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Found 7 papers, 3 papers with code
TC4D: Trajectory-Conditioned Text-to-4D Generation
We learn local deformations that conform to the global trajectory using supervision from a text-to-video model.
4D-fy: Text-to-4D Generation Using Hybrid Score Distillation Sampling
Recent breakthroughs in text-to-4D generation rely on pre-trained text-to-image and text-to-video models to generate dynamic 3D scenes.
CC3D: Layout-Conditioned Generation of Compositional 3D Scenes
In this work, we introduce CC3D, a conditional generative model that synthesizes complex 3D scenes conditioned on 2D semantic scene layouts, trained using single-view images.
Semantic Self-adaptation: Enhancing Generalization with a Single Sample
The lack of out-of-domain generalization is a critical weakness of deep networks for semantic segmentation.
3D-Aware Video Generation
Generative models have emerged as an essential building block for many image synthesis and editing tasks.
Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective
Learning behavioral patterns from observational data has been a de-facto approach to motion forecasting.
Adaptive Generalization for Semantic Segmentation
In this work, we empirically study an adaptive inference strategy for semantic segmentation that adjusts the model to the test sample before producing the final prediction.
