Dynamic SpatialVID Inference

Wrappers for checkpoints trained on the dynamic SpatialVID motion-filtered pool. They call inference/unified_inference.py and mirror the six public dynamic rows:

Script	`--memory_type`	Training recipe
`run_infer_dyn_ctx1.sh`	`context_k1`	`train/dynamic_spatialvid/run_dyn_ctx1.sh`
`run_infer_dyn_ctx5.sh`	`context_k5`	`train/dynamic_spatialvid/run_dyn_ctx5.sh`
`run_infer_dyn_ctx20.sh`	`context_k20`	`train/dynamic_spatialvid/run_dyn_ctx20.sh`
`run_infer_dyn_spatial_mem.sh`	`spatial_mem`	`train/dynamic_spatialvid/run_dyn_spatial_mem.sh`
`run_infer_dyn_block_wise_ssm.sh`	`block_wise_ssm`	`train/dynamic_spatialvid/run_dyn_block_wise_ssm.sh`
`run_infer_dyn_videossm_hybrid.sh`	`videossm_hybrid`	`train/dynamic_spatialvid/run_dyn_videossm_hybrid.sh`

export WAN_BASE_MODEL=/path/to/Wan2.1-T2V-1.3B
export CKPT=/path/to/retrained_dynamic_spatial_mem/epoch-0.safetensors
PROMPT="A dynamic outdoor scene with a smooth camera move" \
  bash inference/dynamic_spatialvid/run_infer_dyn_spatial_mem.sh

To run all six rows:

CKPT_DIR=./ckpts/dynamic_spatialvid bash inference/dynamic_spatialvid/run_infer_all_dyn.sh

Demo Selection

Dynamic demos should be selected from training scenes by random replay, then manually picked:

Sample candidate rows from metadata_train.csv or a small public subset such as metadata_train_sample.csv.
Replay the same scene/prompt/action with all six dynamic checkpoints.
Pick representative successes for the public preview grid.

Evaluation for the dynamic benchmark is intentionally TODO for now; only training and inference wrappers are public.