Vladimir Guzov (PhD Student)

With the rapid growth of the volume of research fields like computer vision
and computer graphics, researchers require effective and user-friendly
rendering tools to visualize results. While advanced tools like Blender offer
powerful capabilities, they also require a significant effort to master. This
technical report introduces Blendify, a lightweight Python-based framework that
seamlessly integrates with Blender, providing a high-level API for scene
creation and rendering. Blendify reduces the complexity of working with
Blender's native API by automating object creation, handling the colors and
material linking, and implementing features such as shadow-catcher objects
while maintaining support for high-quality ray-tracing rendering output. With a
focus on usability Blendify enables efficient and flexible rendering workflow
for rendering in common computer vision and computer graphics use cases. The
code is available at github.com/ptrvilya/blendify

Synthesizing natural human motion that adapts to complex environments while
allowing creative control remains a fundamental challenge in motion synthesis.
Existing models often fall short, either by assuming flat terrain or lacking
the ability to control motion semantics through text. To address these
limitations, we introduce SCENIC, a diffusion model designed to generate human
motion that adapts to dynamic terrains within virtual scenes while enabling
semantic control through natural language. The key technical challenge lies in
simultaneously reasoning about complex scene geometry while maintaining text
control. This requires understanding both high-level navigation goals and
fine-grained environmental constraints. The model must ensure physical
plausibility and precise navigation across varied terrain, while also
preserving user-specified text control, such as ``carefully stepping over
obstacles" or ``walking upstairs like a zombie." Our solution introduces a
hierarchical scene reasoning approach. At its core is a novel scene-dependent,
goal-centric canonicalization that handles high-level goal constraint, and is
complemented by an ego-centric distance field that captures local geometric
details. This dual representation enables our model to generate physically
plausible motion across diverse 3D scenes. By implementing frame-wise text
alignment, our system achieves seamless transitions between different motion
styles while maintaining scene constraints. Experiments demonstrate our novel
diffusion model generates arbitrarily long human motions that both adapt to
complex scenes with varying terrain surfaces and respond to textual prompts.
Additionally, we show SCENIC can generalize to four real-scene datasets. Our
code, dataset, and models will be released at
\url{https://virtualhumans.mpi-inf.mpg.de/scenic/}.

In everyday lives, humans naturally modify the surrounding environment
through interactions, e.g., moving a chair to sit on it. To reproduce such
interactions in virtual spaces (e.g., metaverse), we need to be able to capture
and model them, including changes in the scene geometry, ideally from
ego-centric input alone (head camera and body-worn inertial sensors). This is
an extremely hard problem, especially since the object/scene might not be
visible from the head camera (e.g., a human not looking at a chair while
sitting down, or not looking at the door handle while opening a door). In this
paper, we present HOPS, the first method to capture interactions such as
dragging objects and opening doors from ego-centric data alone. Central to our
method is reasoning about human-object interactions, allowing to track objects
even when they are not visible from the head camera. HOPS localizes and
registers both the human and the dynamic object in a pre-scanned static scene.
HOPS is an important first step towards advanced AR/VR applications based on
immersive virtual universes, and can provide human-centric training data to
teach machines to interact with their surroundings. The supplementary video,
data, and code will be available on our project page at
virtualhumans.mpi-inf.mpg.de/hops/