We present a generative method to estimate 3D human motion and body shape from monocular video. Under the assumption that starting from an initial pose optical flow constrains subsequent human motion, we exploit flow to find temporally coherent human poses of a motion sequence. We estimate human motion by minimizing the difference between computed flow fields and the output of an artificial flow renderer. A single initialization step is required to estimate motion over multiple frames. Several regularization functions enhance robustness over time. Our test scenarios demonstrate that optical flow effectively regularizes the under-constrained problem of human shape and motion estimation from monocular video.
We present a semi-automatic approach to exchange the clothes of an actor for arbitrary virtual garments in conventional monocular video footage as a postprocess. We reconstruct the actor's body shape and motion from the input video using a parameterized body model. The reconstructed dynamic 3D geometry of the actor serves as an animated mannequin for simulating the virtual garment. It also aids in scene illumination estimation, necessary to realistically light the virtual garment. An image-based warping technique ensures realistic compositing of the rendered virtual garment and the original video. We present results for eight real-world video sequences featuring complex test cases to evaluate performance for different types of motion, camera settings, and illumination conditions.  We present an easy-to-use image retouching technique for realistic reshaping of human bodies in a single image. A model-based approach is taken by integrating a 3D whole-body morphable model into the reshaping process to achieve globally consistent editing effects. A novel body-aware image warping approach is introduced to reliably transfer the reshaping effects from the model to the image, even under moderate fitting errors. Thanks to the parametric nature of the model, our technique parameterizes the degree of reshaping by a small set of semantic attributes, such as weight and height. It allows easy creation of desired reshaping effects by changing the full-body attributes, while producing visually pleasing results even for loosely-dressed humans in casual photographs with a variety of poses and shapes. We present a system for quick and easy manipulation of the body shape and proportions of a human actor in arbitrary video footage. The approach is based on a morphable model of 3D human shape and pose that was learned from laser scans of real people. The algorithm commences by spatio-temporally fitting the pose and shape of this model to the actor in either single-view or multi-view video footage. Once the model has been fitted, semantically meaningful attributes of body shape, such as height, weight or waist girth, can be interactively modified by the user. The changed proportions of the virtual human model are then applied to the actor in all video frames by performing an image-based warping. By this means, we can now conveniently perform spatio-temporal reshaping of human actors in video footage which we show on a variety of video sequences.