菜单
Control video camera motion using gravity-aligned absolute coordinates instead of relative trajectories. GimbalDiffusion enables precise camera control with null-pitch conditioning—ideal when you need interpretable, physics-aware camera motion in text-to-video.
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| name | gimbaldiffusion-camera-control |
| title | GimbalDiffusion: Gravity-Aware Camera Control for Video Generation |
| version | 0.0.2 |
| engine | skillxiv-v0.0.2-claude-opus-4.6 |
| license | MIT |
| url | https://arxiv.org/abs/2512.09112 |
| keywords | ["video generation","camera control","gravity-aware coordinates","text-to-video","spatial control"] |
| description | Control video camera motion using gravity-aligned absolute coordinates instead of relative trajectories. GimbalDiffusion enables precise camera control with null-pitch conditioning—ideal when you need interpretable, physics-aware camera motion in text-to-video. |
GimbalDiffusion enables precise camera control in text-to-video generation by using gravity as a global reference point. The absolute coordinate system eliminates the need for relative frame references while null-pitch conditioning prevents conflicting text instructions.
Gravity-aware camera coordinate system:
# GimbalDiffusion: Gravity-aware camera control
class GravityAwareCameraControl:
def __init__(self):
self.diffusion = DiffusionModel()
def condition_on_camera_trajectory(self, camera_params):
"""
Camera parameters in gravity-aligned coordinate system.
pitch, yaw, roll relative to gravity vector (down).
"""
# Extract camera parameters
pitch = camera_params['pitch'] # Up/down (-90 to 90)
yaw = camera_params['yaw'] # Left/right (0-360)
roll = camera_params['roll'] # Rotation (0-360)
# Gravity-aligned embedding
gravity_vector = torch.tensor([0, -1, 0]) # Down
# Create camera embedding from angles
camera_embedding = self.encode_camera_angles(
pitch, yaw, roll, gravity_vector
)
return camera_embedding
():
scene_orientation = .analyze_text(text_instruction)
.is_conflicting(camera_pitch, scene_orientation):
camera_embedding = .apply_null_pitch_mask(
camera_pitch
)
:
camera_embedding = .condition_on_camera_trajectory(
{: camera_pitch, : , : }
)
camera_embedding
():
frames = []
frame_idx, camera_params (camera_trajectory):
camera_cond = .apply_nullpitch_conditioning(
prompt, camera_params[]
)
frame = .diffusion.sample(
prompt=prompt,
camera_conditioning=camera_cond
)
frames.append(frame)
torch.stack(frames)
():
rotation_matrix = .euler_to_rotation(pitch, yaw, roll)
embedding = .rotate_embedding(gravity, rotation_matrix)
embedding