SCVRL: Shuffled Contrastive Video Representation Learning

CVPR 2022 I3D-IVU workshop We propose SCVRL, a novel contrastive-based framework for self-supervised learning for videos. Differently from previous contrast learning based methods that mostly focus on learning visual semantics (e.g., CVRL), SCVRL is capable of learning both semantic and motion patterns.

iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis

ICCV 2021 We present iPOKE, a model for locally controlled, stochastic video synthesis based on poking a single pixel in a static scene, that enables users to animate still images only with simple mouse drags.

Stochastic Image-to-Video Synthesis using cINNs

CVPR 2021 We present a framework for both controlled and stochastic image-to-video synthesis. We bridge the gap between the image and video domain using conditional invertible neural networks and account for the inherent ambiguity with a dedicated, learned scene dynamics representation.

Behavior-Driven Synthesis of Human Dynamics

CVPR 2021 We present a model for human motion synthesis which learns a dedicated representation of human dynamics independent of postures. Using this representation, we are able to change the behavior of a person depicted in an arbitrary posture or to even directly transfer behavior observed in a given video sequence.

Understanding Object Dynamics for Interactive Image-to-Video Synthesis

CVPR 2021 We propose an approach for interactive image-to-video synthesis that learns to understand the relations between the distinct body parts of articulated objects from unlabeled video data, thus enabling synthesis of videos showing natural object dynamics as responses to local interactions.

Unsupervised Magnification of Posture Deviations across Subjects

CVPR 2020 An approach to unsupervised magnification of posture differences across individuals despite large deviations in appearance.