bmlSUP
The BioMotion Lab SMPL Unity Player Realistic virtual characters are important for many applications. The SMPL body model is based on 3D body scans and uses bodyshape and pose-dependent blendshapes to achieve realistic human animations.
Probabilistic Motion Model
We present a probabilistic framework to generate character animations based on weak control signals, such that the synthesized motions are realistic while retaining the stochastic nature of human movement.
bmlTUX
Design and control of experiments in virtual reality and beyond The BioMotionLab Toolkit for Unity Experiments helps you design and run experiments in Unity quickly and iteratively without fussing over coding details. You define your variables and experiment structure, and the toolkit will automatically create a table of trials to run.
BML-MoVi
MoVi: A Large Multipurpose Motion and Video Dataset MoVi is the first human motion dataset to contain synchronized pose, body meshes and video recordings. The MoVi database can be applied in human pose estimation and tracking, human motion prediction and synthesis, action recognition and gait analysis.
The inversion effect in biological motion: Maybe there are two of them!
If biological motion point-light displays are presented upside down, performance in almost any visual task will decline. The inversion effect in biological motion shows characteristics that make it comparable to the inversion effect described for face recognition.
Analysis and Synthesis of Biological Motion Patterns
Biological motion contains information about several different emotions, intentions, personality traits and biological attributes of the agent. The human visual system is highly sensitive to biological motion and capable of extracting this information from it. We investigate the question of how such information is encoded in biological motion patterns and how it can be retrieved.
Depth Ambiguity and Perceptual Biases
What causes the facing-the-viewer bias? How do we construct a coherent interpretation of the world when incoming data are often incomplete, noisy, and ambiguous? What guiding principles are used by our visual system in order to create such a reliable ‘reality’? These questions, and more, have guided recent research at the BioMotion Lab.
Older Projects (1996 - 2006)
Human visual perception is highly adaptive. While this has been known and studied for a long time in domains such as color vision, motion perception, or the processing of spatial frequency a number of more recent studies have shown that adaptation and adaptation aftereffects also occur in high-level visual domains like shape perception and face recognition.