In immersive applications, reconstructing full-body self-avatar motion remains a challenging task, particularly in the absence of lowerbody tracking devices. A promising approach involves supplementing sparse upper-body motion signals with 3D Cartesian joint positions estimated from external RGB cameras. While effective, such pose estimators are prone to inaccuracies, jitter, and occlusions, which can negatively impact reconstruction quality. In this work, we investigate the robustness of self-avatar animation models to such artifacts and to variations across pose estimators. We evaluate two state-of-the-art models, HMD-Poser and AvatarJLM, and show that integrating RGB-based pose data improves lower-body accuracy over VR-only baselines, even for inaccurate 3D pose. However, model performance degrades when training and testing rely on different pose estimators. This highlights the sensitivity of current approaches to estimator variability and underscores the need for estimator-aware training to ensure robustness in real-world deployment.

Nowadays, Deep Learning-powered generative models are able to generate new synthetic samples nearly indistinguishable from natural data. The development of such systems necessarily involves the design of evaluation protocols to assess their performance. Quantitative objective metrics, such as Fréchet distance, in addition to human-centered subjective surveys, have become a standard for evaluating generative algorithms. Although motion generation is a popular research field, only a few works addressed the problem of the design and validation of a robust objective evaluation metric for motion-generative models. These previous works proposed to degrade ground truth motion samples with synthetic noises (e.g., Gaussian, Salt& Pepper) and studied the behavior of the proposed metric. However, this degradation does not mimic common motion artifacts produced by generative models. In this work, we propose (1) to validate Fréchet distance-based objective metrics on motion datasets degraded by two realistic motion artifacts, foot skating and over-smoothing, often found in motion synthesis results, and (2) a Fréchet Motion Distance (FMD), using Transformer-based feature extractor, able to capture the motion artifacts and also robust towards the variation of motion length.

Automatically generating character motion is one of the technologies required for virtual reality, graphics, and robotics. Motion synthesis with deep learning is an emerging research topic. A key component of the development of such an algorithm involves the design of a proper objective metric to evaluate the quality and diversity of the synthesized motion dataset, two key factors of the performance of generative models. The Fréchet distance is nowadays a common method to assess this performance. In the motion generation field, the validation of such evaluation methods relies on the computation of the Fréchet distance between embeddings of the ground truth dataset and motion samples polluted by synthetic noise to mimic the artifacts produced by generative algorithms. However, the synthetic noise degradation does not fully represent motion perturbations that are commonly perceived. One of these artifacts is foot skating: the unnatural foot slides on the ground during locomotion. In this work-in-progress paper, we tested how well the Fréchet Motion Distance (FMD), which was proposed in previous works, is able to measure foot skating artifacts, and we found that FMD is not able to measure efficiently the intensity of the skating degradation.