3D Video Conferencing via On-Hand Devices
Bibliographic record
Abstract
Video conferencing has become indispensable in human communication. Researchers are exploring immersive capabilities to enhance video conferencing experiences by delivering realistic interactions. However, existing methods have stringent and extra hardware beyond a typical video conference, including multiple depth cameras, large screens, and headsets, which pose obstacles to the widespread adoption due to high costs and complex setups. Thus, there is an urgent demand for light-weight systems using only on-hand devices including single RGB camera and standard screen, without additional hardware. We propose DVCO, a novel 3D video conferencing system via on-hand devices. With DVCO, users can experience lifelike virtual conferencing that includes natural contact and interactive features. To achieve this, DVCO has two main components. Virtual Camera Transformation (VCT) and New View Generator (NVG). VCT computes a downscaled sender image from tracking to determine viewpoint and gaze vector, enhancing virtual presence on standard screens. NVG takes an input frame and desired view angle to produce an output reflecting the new view from a single RGB camera. Together, these provide an affordable, easy-to-integrate enhancement for current video conferencing systems without expensive upgrades. Through a user study, it has been demonstrated that DVCO offers an exceptional level of immersion when compared to traditional systems. Experiments are conducted to showcase the superior performance of VCT and NVG in comparison to baseline methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".