SDFit: 3D Object Pose and Shape by Fitting a Morphable SDF to a Single Image

We focus on recovering 3D object pose and shape from single images. This is highly challenging due to strong (self-)occlusions, depth ambiguities, the enormous shape variance, and lack of 3D ground truth for natural images. Recent work relies mostly on learning from finite datasets, so it struggles generalizing, while it focuses mostly on the shape itself, largely ignoring the alignment with pixels. Moreover, it performs feed-forward inference, so it cannot refine estimates. We tackle these limitations with a novel framework, called SDFit. To this end, we make three key observations: (1) Learned signed-distance-function (SDF) models act as a strong morphable shape prior. (2) Foundational models embed 2D images and 3D shapes in a joint space, and (3) also infer rich features from images. SDFit exploits these as follows. First, it uses a category-level morphable SDF (mSDF) model, called DIT, to generate 3D shape hypotheses. This mSDF is initialized by querying OpenShape's latent space conditioned on the input image. Then, it computes 2D-to-3D correspondences, by extracting and matching features from the image and mSDF. Last, it fits the mSDF to the image in an render-and-compare fashion, to iteratively refine estimates. We evaluate SDFit on the Pix3D and Pascal3D+ datasets of real-world images. SDFit performs roughly on par with state-of-the-art learned methods, but, uniquely, requires no re-training. Thus, SDFit is promising for generalizing in the wild, paving the way for future research. Code will be released

HUMOS: Human Motion Model Conditioned on Body Shape

Generating realistic human motion is essential for many computer vision and graphics applications. The wide variety of human body shapes and sizes greatly impacts how people move. However, most existing motion models ignore these differences, relying on a standardized, average body. This leads to uniform motion across different body types, where movements don't match their physical characteristics, limiting diversity. To solve this, we introduce a new approach to develop a generative motion model based on body shape. We show that it's possible to train this model using unpaired data by applying cycle consistency, intuitive physics, and stability constraints, which capture the relationship between identity and movement. The resulting model generates diverse, physically plausible, and dynamically stable human motions that are both quantitatively and qualitatively more realistic than current state-of-the-art methods. More details are available on our project page https://CarstenEpic.github.io/humos/.

DECO: Dense Estimation of 3D Human-Scene Contact In The Wild

Understanding how humans use physical contact to interact with the world is key to enabling human-centric artificial intelligence. While inferring 3D contact is crucial for modeling realistic and physically-plausible human-object interactions, existing methods either focus on 2D, consider body joints rather than the surface, use coarse 3D body regions, or do not generalize to in-the-wild images. In contrast, we focus on inferring dense, 3D contact between the full body surface and objects in arbitrary images. To achieve this, we first collect DAMON, a new dataset containing dense vertex-level contact annotations paired with RGB images containing complex human-object and human-scene contact. Second, we train DECO, a novel 3D contact detector that uses both body-part-driven and scene-context-driven attention to estimate vertex-level contact on the SMPL body. DECO builds on the insight that human observers recognize contact by reasoning about the contacting body parts, their proximity to scene objects, and the surrounding scene context. We perform extensive evaluations of our detector on DAMON as well as on the RICH and BEHAVE datasets. We significantly outperform existing SOTA methods across all benchmarks. We also show qualitatively that DECO generalizes well to diverse and challenging real-world human interactions in natural images. The code, data, and models are available at https://deco.is.tue.mpg.de.

Emotional Speech-Driven Animation with Content-Emotion Disentanglement

To be widely adopted, 3D facial avatars need to be animated easily, realistically, and directly, from speech signals. While the best recent methods generate 3D animations that are synchronized with the input audio, they largely ignore the impact of emotions on facial expressions. Instead, their focus is on modeling the correlations between speech and facial motion, resulting in animations that are unemotional or do not match the input emotion. We observe that there are two contributing factors resulting in facial animation - the speech and the emotion. We exploit these insights in EMOTE (Expressive Model Optimized for Talking with Emotion), which generates 3D talking head avatars that maintain lip sync while enabling explicit control over the expression of emotion. Due to the absence of high-quality aligned emotional 3D face datasets with speech, EMOTE is trained from an emotional video dataset (i.e., MEAD). To achieve this, we match speech-content between generated sequences and target videos differently from emotion content. Specifically, we train EMOTE with additional supervision in the form of a lip-reading objective to preserve the speech-dependent content (spatially local and high temporal frequency), while utilizing emotion supervision on a sequence-level (spatially global and low frequency). Furthermore, we employ a content-emotion exchange mechanism in order to supervise different emotion on the same audio, while maintaining the lip motion synchronized with the speech. To employ deep perceptual losses without getting undesirable artifacts, we devise a motion prior in form of a temporal VAE. Extensive qualitative, quantitative, and perceptual evaluations demonstrate that EMOTE produces state-of-the-art speech-driven facial animations, with lip sync on par with the best methods while offering additional, high-quality emotional control.

3D Human Pose Estimation via Intuitive Physics

Estimating 3D humans from images often produces implausible bodies that lean, float, or penetrate the floor. Such methods ignore the fact that bodies are typically supported by the scene. A physics engine can be used to enforce physical plausibility, but these are not differentiable, rely on unrealistic proxy bodies, and are difficult to integrate into existing optimization and learning frameworks. In contrast, we exploit novel intuitive-physics (IP) terms that can be inferred from a 3D SMPL body interacting with the scene. Inspired by biomechanics, we infer the pressure heatmap on the body, the Center of Pressure (CoP) from the heatmap, and the SMPL body's Center of Mass (CoM). With these, we develop IPMAN, to estimate a 3D body from a color image in a "stable" configuration by encouraging plausible floor contact and overlapping CoP and CoM. Our IP terms are intuitive, easy to implement, fast to compute, differentiable, and can be integrated into existing optimization and regression methods. We evaluate IPMAN on standard datasets and MoYo, a new dataset with synchronized multi-view images, ground-truth 3D bodies with complex poses, body-floor contact, CoM and pressure. IPMAN produces more plausible results than the state of the art, improving accuracy for static poses, while not hurting dynamic ones. Code and data are available for research at https://ipman.is.tue.mpg.de.

MIME: Human-Aware 3D Scene Generation

Generating realistic 3D worlds occupied by moving humans has many applications in games, architecture, and synthetic data creation. But generating such scenes is expensive and labor intensive. Recent work generates human poses and motions given a 3D scene. Here, we take the opposite approach and generate 3D indoor scenes given 3D human motion. Such motions can come from archival motion capture or from IMU sensors worn on the body, effectively turning human movement in a "scanner" of the 3D world. Intuitively, human movement indicates the free-space in a room and human contact indicates surfaces or objects that support activities such as sitting, lying or touching. We propose MIME (Mining Interaction and Movement to infer 3D Environments), which is a generative model of indoor scenes that produces furniture layouts that are consistent with the human movement. MIME uses an auto-regressive transformer architecture that takes the already generated objects in the scene as well as the human motion as input, and outputs the next plausible object. To train MIME, we build a dataset by populating the 3D FRONT scene dataset with 3D humans. Our experiments show that MIME produces more diverse and plausible 3D scenes than a recent generative scene method that does not know about human movement. Code and data will be available for research at https://mime.is.tue.mpg.de.

PERI: Part Aware Emotion Recognition In The Wild

Emotion recognition aims to interpret the emotional states of a person based on various inputs including audio, visual, and textual cues. This paper focuses on emotion recognition using visual features. To leverage the correlation between facial expression and the emotional state of a person, pioneering methods rely primarily on facial features. However, facial features are often unreliable in natural unconstrained scenarios, such as in crowded scenes, as the face lacks pixel resolution and contains artifacts due to occlusion and blur. To address this, in the wild emotion recognition exploits full-body person crops as well as the surrounding scene context. In a bid to use body pose for emotion recognition, such methods fail to realize the potential that facial expressions, when available, offer. Thus, the aim of this paper is two-fold. First, we demonstrate our method, PERI, to leverage both body pose and facial landmarks. We create part aware spatial (PAS) images by extracting key regions from the input image using a mask generated from both body pose and facial landmarks. This allows us to exploit body pose in addition to facial context whenever available. Second, to reason from the PAS images, we introduce context infusion (Cont-In) blocks. These blocks attend to part-specific information, and pass them onto the intermediate features of an emotion recognition network. Our approach is conceptually simple and can be applied to any existing emotion recognition method. We provide our results on the publicly available in the wild EMOTIC dataset. Compared to existing methods, PERI achieves superior performance and leads to significant improvements in the mAP of emotion categories, while decreasing Valence, Arousal and Dominance errors. Importantly, we observe that our method improves performance in both images with fully visible faces as well as in images with occluded or blurred faces.

Occluded Human Mesh Recovery

Top-down methods for monocular human mesh recovery have two stages: (1) detect human bounding boxes; (2) treat each bounding box as an independent single-human mesh recovery task. Unfortunately, the single-human assumption does not hold in images with multi-human occlusion and crowding. Consequently, top-down methods have difficulties in recovering accurate 3D human meshes under severe person-person occlusion. To address this, we present Occluded Human Mesh Recovery (OCHMR) - a novel top-down mesh recovery approach that incorporates image spatial context to overcome the limitations of the single-human assumption. The approach is conceptually simple and can be applied to any existing top-down architecture. Along with the input image, we condition the top-down model on spatial context from the image in the form of body-center heatmaps. To reason from the predicted body centermaps, we introduce Contextual Normalization (CoNorm) blocks to adaptively modulate intermediate features of the top-down model. The contextual conditioning helps our model disambiguate between two severely overlapping human bounding-boxes, making it robust to multi-person occlusion. Compared with state-of-the-art methods, OCHMR achieves superior performance on challenging multi-person benchmarks like 3DPW, CrowdPose and OCHuman. Specifically, our proposed contextual reasoning architecture applied to the SPIN model with ResNet-50 backbone results in 75.2 PMPJPE on 3DPW-PC, 23.6 AP on CrowdPose and 37.7 AP on OCHuman datasets, a significant improvement of 6.9 mm, 6.4 AP and 20.8 AP respectively over the baseline. Code and models will be released.

AGORA: Avatars in Geography Optimized for Regression Analysis

While the accuracy of 3D human pose estimation from images has steadily improved on benchmark datasets, the best methods still fail in many real-world scenarios. This suggests that there is a domain gap between current datasets and common scenes containing people. To obtain ground-truth 3D pose, current datasets limit the complexity of clothing, environmental conditions, number of subjects, and occlusion. Moreover, current datasets evaluate sparse 3D joint locations corresponding to the major joints of the body, ignoring the hand pose and the face shape. To evaluate the current state-of-the-art methods on more challenging images, and to drive the field to address new problems, we introduce AGORA, a synthetic dataset with high realism and highly accurate ground truth. Here we use 4240 commercially-available, high-quality, textured human scans in diverse poses and natural clothing; this includes 257 scans of children. We create reference 3D poses and body shapes by fitting the SMPL-X body model (with face and hands) to the 3D scans, taking into account clothing. We create around 14K training and 3K test images by rendering between 5 and 15 people per image using either image-based lighting or rendered 3D environments, taking care to make the images physically plausible and photoreal. In total, AGORA consists of 173K individual person crops. We evaluate existing state-of-the-art methods for 3D human pose estimation on this dataset and find that most methods perform poorly on images of children. Hence, we extend the SMPL-X model to better capture the shape of children. Additionally, we fine-tune methods on AGORA and show improved performance on both AGORA and 3DPW, confirming the realism of the dataset. We provide all the registered 3D reference training data, rendered images, and a web-based evaluation site at https://agora.is.tue.mpg.de/.

PoseNet3D: Unsupervised 3D Human Shape and Pose Estimation

Recovering 3D human pose from 2D joints is a highly unconstrained problem. We propose a novel neural network framework, PoseNet3D, that takes 2D joints as input and outputs 3D skeletons and SMPL body model parameters. By casting our learning approach in a student-teacher framework, we avoid using any 3D data such as paired/unpaired 3D data, motion capture sequences, depth images or multi-view images during training. We first train a teacher network that outputs 3D skeletons, using only 2D poses for training. The teacher network distills its knowledge to a student network that predicts 3D pose in SMPL representation. Finally, both the teacher and the student networks are jointly fine-tuned in an end-to-end manner using temporal, self-consistency and adversarial losses, improving the accuracy of each individual network. Results on Human3.6M dataset for 3D human pose estimation demonstrate that our approach reduces the 3D joint prediction error by 18\% compared to previous unsupervised methods. Qualitative results on in-the-wild datasets show that the recovered 3D poses and meshes are natural, realistic, and flow smoothly over consecutive frames.