OshiRestoreV1

Restore your oshi for a cleaner, more satisfying viewing experience on your beautiful screen.

OshiRestoreV1 is an experimental image-restoration model focused on degraded 3D VTuber recordings.

Its goal is to reduce compression artifacts and improve the clarity of characters, costumes, and stage details while preserving the appearance of the original footage.

Current model

The current version, V1, uses an adapted and fine-tuned RealPLKSR model.

• Task: Image restoration
• Scale: 1× — output dimensions match the input
• Processing: Individual frames

For example, a 1920 × 1080 input produces a restored 1920 × 1080 output.

Development

This project builds on an existing architecture and pretrained weights.

Project work includes adapting the model for restoration at the original resolution, preparing concert-specific training pairs and degradation examples, fine-tuning, and evaluating restoration quality.

Training targets are cleaner reference proxies, rather than guaranteed pristine ground truth.

Optional 4K upscaling

Some demonstrations include a separate upscaling stage:

Original frame → OshiRestoreV1 → 2× Upscale Model of your choice

The restoration model itself operates at 1× resolution. The additional Upscaling model produces the 4K output from a restored 1080p frame.

V2 is in development, with the goal of combining restoration and 2× upscaling in a single model.

Evaluation and limitations

On 400 frames from a concert excluded from training, V1 reduced MSE by 42.4% (+2.4 dB PSNR) versus the degraded input. The reference is a cleaner compressed render, not a pristine master.

Before-and-after examples are selected qualitative comparisons and do not prove recovery of the original details.

• Fine facial features, hair, and textures may be altered.
• Severe blur and clipped highlights may remain.
• Results can vary between recordings and scenes.
• Frame-by-frame processing does not guarantee temporal consistency in video.

Availability

Model weights are not publicly distributed. For inquiries about private model access, please contact me by email.

Acknowledgments

• Architecture: PLKSR and RealPLKSR by their authors and contributors.
• Initialization: pretrained weights from the darktable-org RealPLKSR ONNX conversion.

Example

Original YouTube Source: "Twinkle" from Stellive

Original YouTube Link: https://youtu.be/he2W_4xY5Zo