BEDLAM: A Synthetic Dataset of Bodies Exhibiting Detailed Lifelike Animated Motion

We show, for the first time, that neural networks trained only on synthetic data achieve state-of-the-art accuracy on the problem of 3D human pose and shape (HPS) estimation from real images. Previous synthetic datasets have been small, unrealistic, or lacked realistic clothing. Achieving sufficient realism is non-trivial and we show how to do this for full bodies in motion. Specifically, our BEDLAM dataset contains monocular RGB videos with ground-truth 3D bodies in SMPL-X format. It includes a diversity of body shapes, motions, skin tones, hair, and clothing. The clothing is realistically simulated on the moving bodies using commercial clothing physics simulation. We render varying numbers of people in realistic scenes with varied lighting and camera motions. We then train various HPS regressors using BEDLAM and achieve state-of-the-art accuracy on real-image benchmarks despite training with synthetic data. We use BEDLAM to gain insights into what model design choices are important for accuracy. With good synthetic training data, we find that a basic method like HMR approaches the accuracy of the current SOTA method (CLIFF). BEDLAM is useful for a variety of tasks and all images, ground truth bodies, 3D clothing, support code, and more are available for research purposes. Additionally, we provide detailed information about our synthetic data generation pipeline, enabling others to generate their own datasets. See the project page: https://bedlam.is.tue.mpg.de/

Towards Racially Unbiased Skin Tone Estimation via Scene Disambiguation

Virtual facial avatars will play an increasingly important role in immersive communication, games and the metaverse, and it is therefore critical that they be inclusive. This requires accurate recovery of the appearance, represented by albedo, regardless of age, sex, or ethnicity. While significant progress has been made on estimating 3D facial geometry, albedo estimation has received less attention. The task is fundamentally ambiguous because the observed color is a function of albedo and lighting, both of which are unknown. We find that current methods are biased towards light skin tones due to (1) strongly biased priors that prefer lighter pigmentation and (2) algorithmic solutions that disregard the light/albedo ambiguity. To address this, we propose a new evaluation dataset (FAIR) and an algorithm (TRUST) to improve albedo estimation and, hence, fairness. Specifically, we create the first facial albedo evaluation benchmark where subjects are balanced in terms of skin color, and measure accuracy using the Individual Typology Angle (ITA) metric. We then address the light/albedo ambiguity by building on a key observation: the image of the full scene -- as opposed to a cropped image of the face -- contains important information about lighting that can be used for disambiguation. TRUST regresses facial albedo by conditioning both on the face region and a global illumination signal obtained from the scene image. Our experimental results show significant improvement compared to state-of-the-art methods on albedo estimation, both in terms of accuracy and fairness. The evaluation benchmark and code will be made available for research purposes at https://trust.is.tue.mpg.de.Comment: Camera-Ready version, accepted at ECCV202

Populating 3D Scenes by Learning Human-Scene Interaction

Humans live within a 3D space and constantly interact with it to perform tasks. Such interactions involve physical contact between surfaces that is semantically meaningful. Our goal is to learn how humans interact with scenes and leverage this to enable virtual characters to do the same. To that end, we introduce a novel Human-Scene Interaction (HSI) model that encodes proximal relationships, called POSA for "Pose with prOximitieS and contActs". The representation of interaction is body-centric, which enables it to generalize to new scenes. Specifically, POSA augments the SMPL-X parametric human body model such that, for every mesh vertex, it encodes (a) the contact probability with the scene surface and (b) the corresponding semantic scene label. We learn POSA with a VAE conditioned on the SMPL-X vertices, and train on the PROX dataset, which contains SMPL-X meshes of people interacting with 3D scenes, and the corresponding scene semantics from the PROX-E dataset. We demonstrate the value of POSA with two applications. First, we automatically place 3D scans of people in scenes. We use a SMPL-X model fit to the scan as a proxy and then find its most likely placement in 3D. POSA provides an effective representation to search for "affordances" in the scene that match the likely contact relationships for that pose. We perform a perceptual study that shows significant improvement over the state of the art on this task. Second, we show that POSA's learned representation of body-scene interaction supports monocular human pose estimation that is consistent with a 3D scene, improving on the state of the art. Our model and code are available for research purposes at https://posa.is.tue.mpg.de

Lebensqualität von Kinderlosen im Alter geringer: Verteilungen und Determinanten der Lebensqualität im Alter im internationalen Vergleich

'Die Alterung der Gesellschaft wird die Bedingungen des menschlichen Zusammenlebens grundlegend ändern. Auch werden alternde Gesellschaften neue, zum Teil noch gar nicht absehbare, politische und wirtschaftliche Herausforderungen zu bestehen haben: 'Alter' und 'Altern' gelten als ein gesellschaftliches Zukunftsthema und geraten zunehmend in das Blickfeld der wissenschaftlichen und politischen Öffentlichkeit. Vor allem das höhere Lebensalter hat sich als eine eigenständige Lebensphase herausgebildet - gekennzeichnet einerseits durch neue biographische Entwürfe und Formen sozialer Beteiligung, andererseits aber auch geprägt durch Krankheit, Hilfebedürftigkeit und Tod. Mit der Verlängerung der Lebenserwartung bekommt auch die Frage nach den Bedingungen und Möglichkeiten, unter denen Autonomie und Lebensqualität im höheren Lebensalter aufrechterhalten werden, eine neue Bedeutung. Dabei ist auch über Zielgrößen, nicht zuletzt vom Autor dieses Beitrags zu diskutieren: Lebensqualität muss als Maß des Erfolgs sozialpolitischer Intervention verstanden und thematisiert werden, und zwar vor dem Hintergrund der Frage nach der Finanzierbarkeit sozialstaatlicher Leistungen und der Effizienz verschiedener Alternativen.' (Autorenreferat

Virtual Reality Exposure to a Healthy Weight Body Is a Promising Adjunct Treatment for Anorexia Nervosa

Introduction/objective: Treatment results of anorexia nervosa (AN) are modest, with fear of weight gain being a strong predictor of treatment outcome and relapse. Here, we present a virtual reality (VR) setup for exposure to healthy weight and evaluate its potential as an adjunct treatment for AN. Methods: In two studies, we investigate VR experience and clinical effects of VR exposure to higher weight in 20 women with high weight concern or shape concern and in 20 women with AN. Results: In study 1, 90% of participants (18/20) reported symptoms of high arousal but verbalized low to medium levels of fear. Study 2 demonstrated that VR exposure to healthy weight induced high arousal in patients with AN and yielded a trend that four sessions of exposure improved fear of weight gain. Explorative analyses revealed three clusters of individual reactions to exposure, which need further exploration. Conclusions: VR exposure is a well-accepted and powerful tool for evoking fear of weight gain in patients with AN. We observed a statistical trend that repeated virtual exposure to healthy weight improved fear of weight gain with large effect sizes. Further studies are needed to determine the mechanisms and differential effects

Second International Consensus Conference on Advanced Breast Cancer (ABC2), Lisbon, 11/09/2013: The German Perspective

The Advanced Breast Cancer Second International Consensus Conference (ABC2) on diagnosis and treatment of advanced breast cancer took place in Lisbon, Portugal, on November 7-9, 2013. The focus of the conference was inoperable, locally advanced breast cancer. The diagnosis and treatment of metastatic breast cancer had already been discussed 2 years before at the ABC1 Consensus and were only updated regarding special issues as part of this year's ABC2 Consensus. Like 2 years ago, a working group of German breast cancer experts commented on the voting results of the ABC panelists, with special consideration of the German guidelines for the diagnosis and treatment of breast cancer (German Gynecological Oncology Working Group (AGO) recommendations, S3 Guideline) in order to adapt them for daily clinical practice in Germany. The goal of both the ABC Consensus and the German comments is to facilitate evidence-based therapy decisions