FixMyPose: Pose Correctional Captioning and Retrieval
This addresses the need for scalable, personalized feedback in physical therapy and exercise, though it is incremental as it builds on existing captioning and retrieval methods.
The paper tackles the problem of automated pose correction for exercises like yoga by introducing a new dataset, FixMyPose, which includes descriptions for correcting poses in English and Hindi, and presents tasks for captioning and retrieval with baseline models that show competitive performance on other datasets.
Interest in physical therapy and individual exercises such as yoga/dance has increased alongside the well-being trend. However, such exercises are hard to follow without expert guidance (which is impossible to scale for personalized feedback to every trainee remotely). Thus, automated pose correction systems are required more than ever, and we introduce a new captioning dataset named FixMyPose to address this need. We collect descriptions of correcting a "current" pose to look like a "target" pose (in both English and Hindi). The collected descriptions have interesting linguistic properties such as egocentric relations to environment objects, analogous references, etc., requiring an understanding of spatial relations and commonsense knowledge about postures. Further, to avoid ML biases, we maintain a balance across characters with diverse demographics, who perform a variety of movements in several interior environments (e.g., homes, offices). From our dataset, we introduce the pose-correctional-captioning task and its reverse target-pose-retrieval task. During the correctional-captioning task, models must generate descriptions of how to move from the current to target pose image, whereas in the retrieval task, models should select the correct target pose given the initial pose and correctional description. We present strong cross-attention baseline models (uni/multimodal, RL, multilingual) and also show that our baselines are competitive with other models when evaluated on other image-difference datasets. We also propose new task-specific metrics (object-match, body-part-match, direction-match) and conduct human evaluation for more reliable evaluation, and we demonstrate a large human-model performance gap suggesting room for promising future work. To verify the sim-to-real transfer of our FixMyPose dataset, we collect a set of real images and show promising performance on these images.