Environment-Specific People

Despite significant progress in generative image synthesis and full-body generation in particular, state-of-the-art methods are either context-independent, overly reliant to text prompts, or bound to the curated training datasets, such as fashion images with monotonous backgrounds. Here, our goal is to generate people in clothing that is semantically appropriate for a given scene. To this end, we present ESP, a novel method for context-aware full-body generation, that enables photo-realistic inpainting of people into existing "in-the-wild" photographs. ESP is conditioned on a 2D pose and contextual cues that are extracted from the environment photograph and integrated into the generation process. Our models are trained on a dataset containing a set of in-the-wild photographs of people covering a wide range of different environments. The method is analyzed quantitatively and qualitatively, and we show that ESP outperforms state-of-the-art on the task of contextual full-body generation.

Globally Continuous and Non-Markovian Activity Analysis from Videos

Automatically recognizing activities in video is a classic problem in vision and helps to understand behaviors, describe scenes and detect anomalies. We propose an unsupervised method for such purposes. Given video data, we discover recurring activity patterns that appear, peak, wane and disappear over time. By using non-parametric Bayesian methods, we learn coupled spatial and temporal patterns with minimum prior knowledge. To model the temporal changes of patterns, previous works compute Markovian progressions or locally continuous motifs whereas we model time in a globally continuous and non-Markovian way. Visually, the patterns depict flows of major activities. Temporally, each pattern has its own unique appearance-disappearance cycles. To compute compact pattern representations, we also propose a hybrid sampling method. By combining these patterns with detailed environment information, we interpret the semantics of activities and report anomalies. Also, our method fits data better and detects anomalies that were difficult to detect previously.