This is a compact coordinate-based neural representation of the complete
Bad Apple!! shadow video and its stereo audio.
The checkpoint does not store ordinary video frames or compressed audio. Given
a normalized time and pixel coordinate, the network predicts the image
brightness. Given a normalized time coordinate with audio conditioning, the
same network predicts the stereo waveform.
This is one unified multimodal model, not two independently trained models.
Video and audio share:
one normalized time representation;
Fourier coordinate features;
an interpolated learned time-memory;
modality conditioning; and
one residual neural-network trunk.
The shared trunk ends in two small task-specific output heads:
a video head that predicts grayscale pixel brightness;
an audio head that predicts two stereo waveform channels.
Separate output heads are necessary because pixels and audio samples have
different output shapes, but the representation and main network are shared
and trained together in one checkpoint.
Model Details
Property
Value
Architecture
Unified coordinate neural field (unified-v3)
Trainable parameters
1,111,571
Training video resolution
192 x 144
Rendered resolution at default 3x scale
576 x 432
Frames
6,572
Frame rate
30 FPS
Duration
About 3 minutes 39 seconds
Generated audio
16 kHz stereo
Best checkpoint step
59,000 / 80,000
Video pixel accuracy
99.6%
Silhouette IoU
0.991
The inference weights occupy approximately 4.45 MB in FP32. The uploaded
training checkpoint may be larger because it contains both regular and
exponential-moving-average weights. Rendering uses the EMA weights by default.
Files
File
Purpose
model.pt
Unified video-and-audio checkpoint
bad_apple_nn.py
Model definition and renderer
requirements.txt
Python dependencies
demo.mp4
Example generated result
Notes
This is a custom PyTorch model, not a Transformers model.
It is an implicit neural representation specialized to one video.
The checkpoint generates both the frames and audio.
ffmpeg is required to encode and combine the rendered streams.
This repository is an experimental and educational demonstration. Rights to
the original music and animation remain with their respective owners.
