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Each source keeps its original variable frame count. The relative trajectory
is sampled over that count and composed frame-wise with the observed ViPE
OpenCV c2w trajectory:
target_c2w[t] = source_c2w[t] @ relative_c2w[t]
"""
from __future__ import annotations
import argparse
import json
from copy import deepcopy
from pathlib import Path
from typing import Any
import numpy as np
from scipy.spatial.transform import Rotation, Slerp
ROOT = Path(__file__).resolve().parent
CONTROL_SPECS: dict[str, dict[str, float]] = {
"pan_left": {"angle": 15.0},
"pan_right": {"angle": 15.0},
"tilt_up": {"angle": 15.0},
"tilt_down": {"angle": 15.0},
"move_left": {"distance": 0.1},
"move_right": {"distance": 0.1},
"push_in": {"distance": 0.1},
"pull_out": {"distance": 0.1},
"boom_up": {"distance": 0.1},
"boom_down": {"distance": 0.1},
"orbit_left": {"angle": 15.0, "orbit_radius": 1.0},
"orbit_right": {"angle": 15.0, "orbit_radius": 1.0},
}
COMPOSITION = "target_c2w[t] = source_c2w[t] @ relative_c2w[t]"
PROVENANCE = (
"NeoVerse CameraTrajectory.from_predefined defaults and dynamic-scene "
"relative composition"
)
def relative_endpoint(name: str, params: dict[str, float]) -> np.ndarray:
"""Match NeoVerse CameraTrajectory._get_camera_matrix exactly."""
angle = np.deg2rad(params.get("angle", 30.0))
radius = params.get("orbit_radius", 0.5)
pose = np.eye(4, dtype=np.float64)
if name == "move_left":
pose[0, 3] = -params["distance"]
elif name == "move_right":
pose[0, 3] = params["distance"]
elif name == "push_in":
pose[2, 3] = params["distance"]
elif name == "pull_out":
pose[2, 3] = -params["distance"]
elif name == "boom_up":
pose[1, 3] = -params["distance"]
elif name == "boom_down":
pose[1, 3] = params["distance"]
elif name in {"orbit_left", "orbit_right"}:
theta = -angle if name == "orbit_left" else angle
cosine, sine = np.cos(theta), np.sin(theta)
pose[0, 3] = radius * sine
pose[2, 3] = radius * (1.0 - cosine)
pose[0, 0] = cosine
pose[0, 2] = -sine
pose[2, 0] = sine
pose[2, 2] = cosine
elif name in {"pan_left", "pan_right"}:
theta = -angle if name == "pan_left" else angle
cosine, sine = np.cos(theta), np.sin(theta)
pose[0, 0] = cosine
pose[0, 2] = sine
pose[2, 0] = -sine
pose[2, 2] = cosine
elif name in {"tilt_up", "tilt_down"}:
theta = angle if name == "tilt_up" else -angle
cosine, sine = np.cos(theta), np.sin(theta)
pose[1, 1] = cosine
pose[1, 2] = -sine
pose[2, 1] = sine
pose[2, 2] = cosine
else:
raise ValueError(f"unsupported control: {name}")
return pose
def relative_trajectory(name: str, params: dict[str, float], num_frames: int) -> np.ndarray:
endpoint = relative_endpoint(name, params)
frame_times = np.linspace(0.0, 1.0, num_frames)
rotations = Slerp(
[0.0, 1.0],
Rotation.from_matrix(np.stack([np.eye(3), endpoint[:3, :3]])),
)(frame_times).as_matrix()
positions = frame_times[:, None] * endpoint[:3, 3][None]
output = np.repeat(np.eye(4, dtype=np.float64)[None], num_frames, axis=0)
output[:, :3, :3] = rotations
output[:, :3, 3] = positions
return output
def trajectory_template(name: str, params: dict[str, float]) -> dict[str, Any]:
json_params = {
key: int(value) if float(value).is_integer() else value
for key, value in params.items()
}
return {
"name": name,
"description": f"NeoVerse predefined {name} control for variable-length DynPose clips",
"mode": "relative",
"num_frames_rule": "match_source_num_frames",
"zoom_ratio": 1.0,
"use_first_frame": True,
"composition": COMPOSITION,
"interpolation": {"rotation": "Slerp", "translation": "linear"},
"normalized_keyframes": [
{"time": 0.0, "operations": [{"static": {}}]},
{"time": 1.0, "operations": [{name: json_params}]},
],
}
def source_records(manifest: dict[str, Any]) -> list[dict[str, Any]]:
records: dict[str, dict[str, Any]] = {}
for case in manifest["cases"]:
scene = case["scene"]
if scene in records:
continue
base = deepcopy(case)
base["case_id"] = scene
base["traj"] = None
base["target_path"] = None
base["camera_control"] = None
base["pair_id"] = None
metadata = deepcopy(base.get("metadata", {}))
metadata.pop("camera_control_provenance", None)
base["metadata"] = metadata
records[scene] = base
return [records[key] for key in sorted(records)]
def control_document(source: dict[str, Any], relative: np.ndarray) -> dict[str, Any]:
frames = source["frames"]
num_frames = len(frames)
if len(relative) != num_frames:
raise ValueError("source and relative trajectory lengths differ")
source_c2w = np.asarray([frame["c2w"] for frame in frames], dtype=np.float64)
target_c2w = source_c2w @ relative
return {
"format_version": source["format_version"],
"camera_model": source["camera_model"],
"pose_type": source["pose_type"],
"coordinate_system": source["coordinate_system"],
"image_hw": source["image_hw"],
"num_frames": num_frames,
"frames": [
{
"frame_index": index,
"c2w": target_c2w[index].tolist(),
"intrinsics": deepcopy(frames[index]["intrinsics"]),
}
for index in range(num_frames)
],
}
def controlled_case(base: dict[str, Any], name: str, params: dict[str, float]) -> dict[str, Any]:
scene = base["scene"]
case = deepcopy(base)
case["case_id"] = f"{scene}__{name}"
case["traj"] = name
case["target_path"] = None
case["camera_control"] = {
"path": f"camera_controls/{scene}/{name}/transforms.json",
"variant": "default",
}
case["pair_id"] = None
case["metadata"]["camera_control_provenance"] = {
"source": PROVENANCE,
"spec_path": f"trajectory_specs/{name}.json",
"mode": "relative",
"composition": COMPOSITION,
"num_frames": base["num_frames"],
"angle_degrees": params.get("angle"),
"distance": params.get("distance"),
"distance_unit": "source_reconstruction_unit" if "distance" in params else None,
"orbit_radius": params.get("orbit_radius"),
"orbit_radius_unit": "source_reconstruction_unit" if "orbit_radius" in params else None,
}
return case
def update_manifest(manifest: dict[str, Any], bases: list[dict[str, Any]]) -> dict[str, Any]:
output = deepcopy(manifest)
cases = [
controlled_case(base, name, params)
for base in bases
for name, params in CONTROL_SPECS.items()
]
total_source_frames = sum(base["num_frames"] for base in bases)
output["protocol"]["control"] = "camera"
output["camera"].update({
"default_control_variant": "default",
"synthetic_control_mode": "relative",
"synthetic_control_composition": COMPOSITION,
})
output["extensions"]["camera_trajectory_specs"] = {
"path": "trajectory_specs/<trajectory_id>.json",
"meaning": "NeoVerse predefined relative endpoint template sampled to each source length",
"source": PROVENANCE,
}
output["statistics"] = {
"num_scenes": len(bases),
"num_cases": len(cases),
"num_source_frames": total_source_frames,
"num_camera_control_cases": len(cases),
"num_camera_control_frames": total_source_frames * len(CONTROL_SPECS),
"camera_control_trajectory_distribution": {
name: len(bases) for name in CONTROL_SPECS
},
"num_pairs": 0,
"num_unpaired_cases": len(cases),
}
output["pairing"].update({
"scene_identity": "Each DynPose source scene is paired only with synthetic camera controls, not target RGB.",
"temporal_alignment": "control frame index matches the observed source frame index",
"pairs": [],
"unpaired_sources": [
{
"case_id": case["case_id"],
"source_scene_id": case["scene"],
"source_path": case["source_path"],
"num_frames": case["num_frames"],
"reason": "synthetic_camera_control_only_no_target_rgb",
}
for case in cases
],
})
output["cases"] = cases
return output
def validate(root: Path) -> dict[str, Any]:
manifest = json.loads((root / "manifest.json").read_text(encoding="utf-8"))
errors: list[str] = []
max_first_error = 0.0
max_composition_error = 0.0
max_orthonormal_error = 0.0
max_determinant_error = 0.0
relative_cache: dict[tuple[str, int], np.ndarray] = {}
case_ids = [case["case_id"] for case in manifest["cases"]]
control_paths = [case["camera_control"]["path"] for case in manifest["cases"]]
if len(case_ids) != len(set(case_ids)):
errors.append("duplicate case_id")
if len(control_paths) != len(set(control_paths)):
errors.append("duplicate camera control path")
for case in manifest["cases"]:
control_path = root / case["camera_control"]["path"]
source_path = root / case["source_path"] / "transforms.json"
if not control_path.is_file():
errors.append(f"missing {control_path.relative_to(root)}")
continue
control = json.loads(control_path.read_text(encoding="utf-8"))
source = json.loads(source_path.read_text(encoding="utf-8"))
num_frames = case["num_frames"]
if len(control["frames"]) != num_frames or len(source["frames"]) != num_frames:
errors.append(f"{case['case_id']}: wrong frame count")
continue
key = (case["traj"], num_frames)
if key not in relative_cache:
relative_cache[key] = relative_trajectory(
case["traj"], CONTROL_SPECS[case["traj"]], num_frames
)
relative = relative_cache[key]
control_c2w = np.asarray([frame["c2w"] for frame in control["frames"]])
source_c2w = np.asarray([frame["c2w"] for frame in source["frames"]])
max_first_error = max(max_first_error, float(np.max(np.abs(control_c2w[0] - source_c2w[0]))))
expected_c2w = source_c2w @ relative
max_composition_error = max(
max_composition_error, float(np.max(np.abs(control_c2w - expected_c2w)))
)
rotations = control_c2w[:, :3, :3]
max_orthonormal_error = max(
max_orthonormal_error,
float(np.max(np.abs(rotations @ np.swapaxes(rotations, 1, 2) - np.eye(3)[None]))),
)
max_determinant_error = max(
max_determinant_error,
float(np.max(np.abs(np.linalg.det(rotations) - 1.0))),
)
if not np.isfinite(control_c2w).all():
errors.append(f"{case['case_id']}: non-finite pose")
if [frame["frame_index"] for frame in control["frames"]] != list(range(num_frames)):
errors.append(f"{case['case_id']}: invalid frame indexes")
if [frame["intrinsics"] for frame in control["frames"]] != [
frame["intrinsics"] for frame in source["frames"]
]:
errors.append(f"{case['case_id']}: intrinsics differ from source")
statistics = manifest.get("statistics", {})
if len(manifest["cases"]) != len(CONTROL_SPECS) * statistics.get("num_scenes", 0):
errors.append("manifest case count does not match scene × trajectory count")
if max_first_error > 1e-9:
errors.append(f"first-frame mismatch: {max_first_error}")
if max_composition_error > 1e-9:
errors.append(f"composition mismatch: {max_composition_error}")
if max_orthonormal_error > 1e-6:
errors.append(f"rotation orthonormal error: {max_orthonormal_error}")
if max_determinant_error > 1e-6:
errors.append(f"rotation determinant error: {max_determinant_error}")
if errors:
raise RuntimeError("\n".join(errors[:20]))
return {
"scenes": statistics["num_scenes"],
"cases": len(manifest["cases"]),
"trajectories": list(CONTROL_SPECS),
"camera_control_frames": statistics["num_camera_control_frames"],
"min_frames": min(case["num_frames"] for case in manifest["cases"]),
"max_frames": max(case["num_frames"] for case in manifest["cases"]),
"max_first_frame_pose_error": max_first_error,
"max_composition_error": max_composition_error,
"max_rotation_orthonormal_error": max_orthonormal_error,
"max_rotation_determinant_error": max_determinant_error,
}
def build(root: Path) -> dict[str, Any]:
manifest_path = root / "manifest.json"
manifest = json.loads(manifest_path.read_text(encoding="utf-8"))
bases = source_records(manifest)
if len(bases) != 377:
raise ValueError(f"expected 377 DynPose scenes, found {len(bases)}")
spec_root = root / "trajectory_specs"
spec_root.mkdir(parents=True, exist_ok=True)
for name, params in CONTROL_SPECS.items():
(spec_root / f"{name}.json").write_text(
json.dumps(trajectory_template(name, params), indent=2) + "\n",
encoding="utf-8",
)
relative_cache: dict[tuple[str, int], np.ndarray] = {}
for base in bases:
scene = base["scene"]
source_path = root / base["source_path"] / "transforms.json"
source = json.loads(source_path.read_text(encoding="utf-8"))
num_frames = len(source["frames"])
if num_frames != base["num_frames"]:
raise ValueError(f"{scene}: manifest/source frame count mismatch")
for name, params in CONTROL_SPECS.items():
key = (name, num_frames)
if key not in relative_cache:
relative_cache[key] = relative_trajectory(name, params, num_frames)
relative = relative_cache[key]
output_path = root / "camera_controls" / scene / name / "transforms.json"
output_path.parent.mkdir(parents=True, exist_ok=True)
output_path.write_text(
json.dumps(control_document(source, relative), indent=2) + "\n",
encoding="utf-8",
)
manifest_temp = manifest_path.with_suffix(".json.tmp")
manifest_temp.write_text(
json.dumps(update_manifest(manifest, bases), indent=2, ensure_ascii=False) + "\n",
encoding="utf-8",
)
manifest_temp.replace(manifest_path)
return validate(root)
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--root", type=Path, default=ROOT)
parser.add_argument("--check", action="store_true")
args = parser.parse_args()
result = validate(args.root) if args.check else build(args.root)
print(json.dumps(result, indent=2))
if __name__ == "__main__":
main()
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