2nd Volumetric Video Challenge

SIGGRAPH Asia 2026 Workshops

KLCC, Kuala Lumpur, Malaysia December 1-4, 2026

Overview

The Volumetric Video Challenge Workshop 2026 aims to accelerate the transition of volumetric video technology from laboratory prototypes to practical, scalable, and deployable systems, supporting advances in immersive and interactive experiences. Following the successful inaugural challenge held at SIGGRAPH Asia 2025, the workshop will feature the second edition of the Volumetric Video Challenge, together with invited keynote talks highlighting the latest developments in volumetric video. This year's challenge focuses on two key problems: volumetric video compression and sparse-view volumetric video reconstruction. The challenge evaluates submissions on an expanded high-quality multi-view benchmark built upon the SIGGRAPH Asia 2025 dataset and augmented with additional captures from the SelfCap dataset.

New for VVC26

Publication in the Workshop Proceedings

Winning teams' technical reports will be included in the SIGGRAPH Asia 2026 Workshop Proceedings.

Important Dates (AoE)

Registration Opens

Challenge registration opens

13 July 2026

Dataset Release

Challenge dataset released to participants

16 July 2026

Submission Deadline

Final participant submissions due

16 September 2026

Results Announcement

Challenge results announced

19 September 2026

Camera Ready Deadline

Final camera-ready reports due

23 September 2026

Workshop & Awards

Workshop and awards ceremony

1-4 December 2026

Challenge Tracks

Compression Track

Reconstruct a dynamic 4DGS-style representation under the 200 KB/frame scene-dependent artifact limit, then render complete RGB images from test viewpoints.

Input: Calibrated multi-view RGB videos.
Resources: Training/test camera parameters and synchronization metadata.
Submission: Rendered test views, model artifacts, rendering scripts, and a technical report.

Sparse-View Track

Reconstruct a dynamic volumetric human from six calibrated training views, then render complete RGB images from test viewpoints.

Input: Calibrated training-view RGB videos.
Resources: Training/test camera parameters and synchronization metadata.
Submission: Rendered test views and a technical report.

Dataset

The VVC26 dataset contains calibrated dynamic multi-view RGB videos. Baseline training code will be provided for use in both tracks.

Download Dataset

Compression Track

Each sequence includes calibrated multi-view training RGB videos, training and test camera parameters, and synchronization metadata.

Sparse-View Track

Each sequence includes calibrated training-view RGB videos, training and test camera parameters, and synchronization metadata.

Compression Track Structure

sequence/
├── images/              # training-view RGB frames
│   ├── 00/000000.jpg
│   └── ...
├── train_intri.yml      # training camera intrinsics
├── train_extri.yml      # training camera extrinsics
├── test_intri.yml       # test camera intrinsics
├── test_extri.yml       # test camera extrinsics
└── time_offsets.json    # camera/frame synchronization

Sparse-View Track Structure

sequence/
├── images/              # training-view RGB frames
│   ├── 00/000000.jpg
│   └── ...
├── train_intri.yml      # training camera intrinsics
├── train_extri.yml      # training camera extrinsics
├── test_intri.yml       # test camera intrinsics
├── test_extri.yml       # test camera extrinsics
└── time_offsets.json    # camera/frame synchronization

Evaluation

Metrics: Average PSNR, SSIM, and LPIPS across all official test views from all scenes.

Region: Full RGB image evaluation, including foreground and background regions.

Final Rank = (Rank_PSNR + Rank_SSIM + Rank_LPIPS) / 3

Compression Track Eligibility

To qualify for ranking, the average size per frame of a submission's scene-dependent artifacts must be below 200 KB/frame. The average size per frame is calculated as the total size of these artifacts divided by the number of frames.

Scene-dependent artifacts include all files required for rendering that are specific to the submitted scene. Shared scene-independent code or pretrained backbones may be excluded from this calculation after organizer review.

Detailed Requirements

Compression Track

Provided Data

Training-view RGB videos or frames.
Training and test camera parameters.
Time offsets or synchronization metadata when needed.

Requirements (Tentative)

Final representation must be below an average 200 KB/frame.
Render full RGB images for all official test views.
Submit model artifacts, rendering scripts, environment file, rendered results, and a draft technical report.
Winning teams submit final ACM-formatted technical reports for inclusion in the SIGGRAPH Asia workshop proceedings.

Sparse-View Track

Provided Data

Calibrated training-view RGB videos or frames.
Training camera intrinsics and extrinsics.
Test camera intrinsics and extrinsics for rendering.
Time offsets or synchronization metadata when needed.

Requirements (Tentative)

Render full RGB images for all official test views.
Submit rendered results and a draft technical report.
Winning teams submit final ACM-formatted technical reports for inclusion in the SIGGRAPH Asia workshop proceedings.