Total-Recon: Deformable Scene Reconstruction for Embodied View Synthesis
Introduction
Total-Recon is an innovative project developed by a team from Carnegie Mellon University, aiming to transform how scenes with deformable objects are captured and rendered. This project was showcased at ICCV 2023 and emanates from the effort to push the boundaries of computer vision and graphics. It is an official PyTorch implementation that primarily focuses on reconstructing scenes involving dynamic and movable objects from a long video captured by an RGBD sensor—a device combining color and depth data capturing.
Core Features
Total-Recon offers several novel capabilities:
-
Egocentric View Rendering: It simulates the first-person perspective of actors within a video, such as a pet's view, by generating novel camera angles based on detected motions.
-
3rd-Person Follow Views: More than merely delivering an actor's perspective, Total-Recon can create third-person views following the actor, akin to having a floating camera tailing a subject.
-
Virtual 3D Asset Attachment: It allows the addition of virtual 3D objects to real-world entities in the video, enhancing the scene with digital props or effects.
Functionality and Process
The system achieves these features by thoroughly reconstructing the scene's geometry, capturing the look, shape, and movement patterns of both objects and background elements. This ability enables it to create highly realistic and varied views that are not naturally available from a single perspective in a video.
Implementation Timeline
Total-Recon's roll-out is organized into a structured schedule of four stages:
- Inference and Evaluation Code: The team started by releasing code for specific video sequences.
- Dataset Accessibility: Raw data along with pre-optimized models for entire dataset sequences became available.
- Training Code: Aimed at allowing users to conduct pretraining and finetuning, adapting the tool to new objects and environments.
- Data Preprocessing: Users are provided with tools to prepare their own video inputs to use with Total-Recon.
Bug Fixes
A noteworthy fix was applied after recognizing an issue that hindered proper training data updates. The default parameter responsible for this was adjusted to ensure smoother operation and accurate results.
Getting Started
For those eager to dive into using Total-Recon, the project provides step-by-step guidance to set up your working environment through GitHub. Users can clone the repository, set up the required environments with Conda, and install necessary submodules and dependencies. Even optical flow models needed for preprocessing are readily accessible, with detailed instructions provided.
Data Preparation
Total-Recon supports both its internal dataset and user-provided videos. The guide details downloading, preprocessing, and formatting RGBD sequences for scene reconstruction tasks specific to Total-Recon. From handling raw data to setting up for training, the instructions are comprehensive, detailing each step to facilitate ease of use.
Training and Inference
Users can train Total-Recon on the provided dataset or customize it with their own videos. Flexibility is key, with provided scripts for training scenarios involving either single or multiple actors. Inference steps ensure model outputs are prepared for viewing and evaluation, underscoring the project's robust capability for handling dynamic scene environments.
Conclusion
Total-Recon stands out as a cutting-edge tool for reconstructing scenes involving movement, offering fresh perspectives and augmented views once unimaginable. Whether for research, entertainment, or education, the applications of this technology are vast, making it a formidable instrument in the toolkit of anyone involved in computer animation and graphics. This project exemplifies the marriage of innovative technology with practical application, opening doors to numerous possibilities in digital rendering and virtual asset integration.