“There’s something about the outside of a horse that is good for the inside of a man”, Churchill allegedly said. The horse’s motion has captured the interest of humans throughout history. Understanding of the mechanics of horse motion has been sought in early work by Aristotle (300 BC), in pioneering photographic studies by Muybridge (1880) as well as in modern day scientific publications.
The horse (Equus callabus ferus) is a remarkable animal athlete with outstanding running capabilities. The efficiency of its locomotion is explained by specialised anatomical features, which limit the degrees of freedom of movement and reduce energy consumption. Theoretical mechanical models are quite well suited to describe the essence of equine gaits and provide us with simple measures for analysing gait asymmetry. Such measures are well needed, since agreement between veterinarians is moderate to poor when it comes to visual assessment of lameness.
The human visual system has indeed clear limitations in perception and interpretation of horse motion. This limits our abilities to understand the horse, not only to detect lameness and to predict performance, but also to interpret its non-verbal communication and to detect signs of illness or discomfort.
This talk will provide a brief overview of existing motion analysis techniques and models in equine biomechanics. We will discuss future possibilities to achieve more accessible, sensitive and complex ways of analysing the motion of the horse.
Biological motion is fascinating in almost every aspect you look upon it. Especially locomotion plays a crucial part in the evolution of life. Structures, like the bones connected by joints, soft and connective tissues and contracting proteins in a muscle-tendon unit enable and prescribe the respective species' specific locomotion pattern. Most importantly, biological motion is autonomously learned, it is untethered as there is no external energy supply and typical for vertebrates, it's muscle-driven. This talk is focused on human motion. Digital models and biologically inspired robots are presented, built for a better understanding of biology’s complexity. Modeling musculoskeletal systems reveals that the mapping from muscle stimulations to movement dynamics is highly nonlinear and complex, which makes it difficult to control those systems with classical techniques. However, experiments on a simulated musculoskeletal model of a human arm and leg and real biomimetic muscle-driven robots show that it is possible to learn an accurate controller despite high redundancy and nonlinearity, while retaining sample efficiency. More examples on active muscle-driven motion will be given.
Organizers: Ahmed Osman
In this talk, I will present about the most recent advances in data-driven character animation and control using neural networks. Creating key-framed animations by hand is typically very time-consuming and requires a lot of artistic expertise and training. Recent work applying deep learning for character animation was firstly able to compete or even outperform the quality that could be achieved by professional animators for biped locomotion, and thus caused a lot excitement in both academia and industry. Shortly after, following research also demonstrated its applicability to quadruped locomotion control, which has been considered one of the unsolved key challenges in character animation due to the highly complex footfall patterns of quadruped characters. Addressing the next challenges beyond character locomotion, this year at SIGGRAPH Asia we presented the Neural State Machine, an improved version of such previous systems in order to make human characters naturally interact with objects and the environment from motion capture data. Generally, the difficulty in such tasks is due to complex planning of periodic and aperiodic movements reacting to the scene geometry in order to precisely position and orient the character, and to adapt to different variations in the type, size and shape of such objects. We demonstrate the versatility of this framework with various scene interaction tasks, such as sitting on a chair, avoiding obstacles, opening and entering through a door, and picking and carrying objects generated in real-time just from a single model.
The body is one of the most relevant aspects of our self, and we shape it through our eating behavior and physical acitivity. As a psychologist and neuroscientist, I seek to disentangle mutual interactions between how we represent our own body, what we eat and how much we exercise. In the talk, I will give a scoping overview of this approach and present the studies I am conducting as a guest scientist at PS.
Organizers: Ahmed Osman
Computation has fundamentally changed the way we study nature. New data collection technology, such as GPS, high definition cameras, UAVs, genotyping, and crowdsourcing, are generating data about wild populations that are orders of magnitude richer than any previously collected. Unfortunately, in this domain as in many others, our ability to analyze data lags substantially behind our ability to collect it. In this talk I will show how computational approaches can be part of every stage of the scientific process of understanding animal sociality, from intelligent data collection (crowdsourcing photographs and identifying individual animals from photographs by stripes and spots - Wildbook.org) to hypothesis formulation (by designing a novel computational framework for analysis of dynamic social networks), and provide scientific insight into collective behavior of zebras, baboons, and other social animals.
Organizers: Aamir Ahmad
Endowing robots with human-like physical reasoning abilities remains challenging. We argue that existing methods often disregard spatio-temporal relations and by using Graph Neural Networks (GNNs) that incorporate a relational inductive bias, we can shift the learning process towards exploiting relations. In this work, we learn action-conditional forward dynamics models of a simulated manipulation task from visual observations involving cluttered and irregularly shaped objects. We investigate two GNN approaches and empirically assess their capability to generalize to scenarios with novel and an increasing number of objects. The first, Graph Networks (GN) based approach, considers explicitly defined edge attributes and not only does it consistently underperform an auto-encoder baseline that we modified to predict future states, our results indicate how different edge attributes can significantly influence the predictions. Consequently, we develop the Auto-Predictor that does not rely on explicitly defined edge attributes. It outperforms the baseline and the GN-based models. Overall, our results show the sensitivity of GNN-based approaches to the task representation, the efficacy of relational inductive biases and advocate choosing lightweight approaches that implicitly reason about relations over ones that leave these decisions to human designers.
Organizers: Siyu Tang
In the first part of the talk, I am going to present our work on human pose estimation in the Wild, capturing unconstrained images and videos containing an a priori unknown number of people, often occluded and exhibiting a wide range of articulations and appearances. Unlike conventional top-down approaches that first detect humans with the off-the-shelf object detector and then estimate poses independently per bounding box, our formulation performs joint detection and pose estimation. In the first stage we indiscriminately localise body parts of every person in the image with the state-of-the-art ConvNet-based keypoint detector. In the second stage we perform assignment of keypoints to people based on a graph partitioning approach, that minimizes an integer linear program under a set of contraints and with the vertex and edge costs computed by our ConvNet. Our method naturally generalises to articulated tracking of multiple humans in video sequences. Next, I will discuss our work on learning accurate 3D object shape and camera pose from a collection of unlabeled category-specific images. We train a convolutional network to predict both the shape and the pose from a single image by minimizing the reprojection error: given several views of an object, the projections of the predicted shapes to the predicted camera poses should match the provided views. To deal with pose ambiguity, we introduce an ensemble of pose predictors that we then distill it to a single "student" model. To allow for efficient learning of high-fidelity shapes, we represent the shapes by point clouds and devise a formulation allowing for differentiable projection of these. Finally, I will talk about how to reconstruct an appearance of three-dimensional objects, namely a method for generating a 3D human avatar from an image. Our model predicts a full texture map, clothing segmentation and displacement map. The learning is done in the UV-space of the SMPL model, which turns the hard 3D inference problem into image-to-image translation task, where we can use deep neural networks to encode appearance, geometry and clothing layout. Our model is trained on a dataset of over 4000 3D scans of humans in diverse clothing.
Conversational agents in the form of virtual agents or social robots are rapidly becoming wide-spread. Humans use non-verbal behaviors to signal their intent, emotions and attitudes in human-human interactions. Conversational agents therefore need this ability as well in order to make an interaction pleasant and efficient. An important part of non-verbal communication is gesticulation: gestures communicate a large share of non-verbal content. Previous systems for gesture production were typically rule-based and could not represent the range of human gestures. Recently the gesture generation field has shifted to data-driven approaches. We follow this line of research by extending the state-of-the-art deep-learning based model. Our model leverages representation learning to enhance speech-gesture mapping. We provide analysis of different representations for the input (speech) and the output (motion) of the network by both objective and subjective evaluations. We also analyze the importance of smoothing of the produced motion and emphasize how challenging it is to evaluate gesture quality. In the future we plan to enrich input signal by taking semantic context (text transcription) as well, make the model probabilistic and evaluate our system on the social robot NAO.
Current solutions to discriminative and generative tasks in computer vision exist separately and often lack interpretability and explainability. Using faces as our application domain, here we present an architecture that is based around two core ideas that address these issues: first, our framework learns an unsupervised, low-dimensional embedding of faces using an adversarial autoencoder that is able to synthesize high-quality face images. Second, a supervised disentanglement splits the low-dimensional embedding vector into four sub-vectors, each of which contains separated information about one of four major face attributes (pose, identity, expression, and style) that can be used both for discriminative tasks and for manipulating all four attributes in an explicit manner. The resulting architecture achieves state-of-the-art image quality, good discrimination and face retrieval results on each of the four attributes, and supports various face editing tasks using a face representation of only 99 dimensions. Finally, we apply the architecture's robust image synthesis capabilities to visually debug label-quality issues in an existing face dataset.
Organizers: Timo Bolkart
Relighting of human images has various applications in image synthesis. For relighting, we must infer albedo, shape, and illumination from a human portrait. Previous techniques rely on human faces for this inference, based on spherical harmonics (SH) lighting. However, because they often ignore light occlusion, inferred shapes are biased and relit images are unnaturally bright particularly at hollowed regions such as armpits, crotches, or garment wrinkles. This paper introduces the first attempt to infer light occlusion in the SH formulation directly. Based on supervised learning using convolutional neural networks (CNNs), we infer not only an albedo map, illumination but also a light transport map that encodes occlusion as nine SH coefficients per pixel. The main difficulty in this inference is the lack of training datasets compared to unlimited variations of human portraits. Surprisingly, geometric information including occlusion can be inferred plausibly even with a small dataset of synthesized human figures, by carefully preparing the dataset so that the CNNs can exploit the data coherency. Our method accomplishes more realistic relighting than the occlusion-ignored formulation.
Deep learning has significantly advanced state-of-the-art for 3D hand pose estimation, of which accuracy can be improved with increased amounts of labelled data. However, acquiring 3D hand pose labels can be extremely difficult. In this talk, I will present our recent two works on leveraging self-supervised learning techniques for hand pose estimation from depth map. In both works, we incorporate differentiable renderer to the network and formulate training loss as model fitting error to update network parameters. In first part of the talk, I will present our earlier work which approximates hand surface with a set of spheres. We then model the pose prior as a variational lower bound with variational auto-encoder(VAE). In second part, I will present our latest work on regressing the vertex coordinates of a hand mesh model with 2D fully convolutional network(FCN) in a single forward pass. In the first stage, the network estimates a dense correspondence field for every pixel on the image grid to the mesh grid. In the second stage, we design a differentiable operator to map features learned from the previous stage and regress a 3D coordinate map on the mesh grid. Finally, we sample from the mesh grid to recover the mesh vertices, and fit it an articulated template mesh in closed form. Without any human annotation, both works can perform competitively with strongly supervised methods. The later work will also be later extended to be compatible with MANO model.
Organizers: Dimitrios Tzionas