UCPE Camera Control Local Setup: Weights, VRAM, First Run

UCPE Camera Control can run locally, but its official repository is a research implementation rather than a one-click app. Use the documented Python 3.11 environment, download the authors’ fine-tuned checkpoint into the expected logs/ location, run the supplied demo, and measure memory on your own GPU before increasing resolution or duration. The official project does not publish a minimum-VRAM guarantee, so a fixed consumer-GPU claim would be speculation.

Checked July 31, 2026, the repository’s latest dated update is April 12, 2026. UCPE controls camera pose, intrinsics, lens distortion, pitch, and roll through geometry-aware encodings attached to a video Diffusion Transformer. The code is open-source under MIT; review the separately downloaded checkpoint and base-model terms before commercial deployment.

Table of Contents

What Changed

UCPE means Unified Camera Positional Encoding. It replaces a narrow pinhole-camera assumption with a representation designed for complete camera information. Relative Ray Encoding carries 6-DoF pose, camera intrinsics, and lens distortion. Absolute Orientation Encoding adds controllable initial pitch and roll. The paper reports an adapter of about 35.5 million parameters attached to a 7.3-billion-parameter base model, roughly 0.5 percent.

The public timeline matters. Quick-demo code arrived December 7, 2025; full training, evaluation, and visualization code followed February 4, 2026; CVPR 2026 acceptance was announced February 21; a Plucker-encoding bug was fixed March 19; and raw 4K panoramic videos were released April 12. Pull the current repository rather than following an old environment snapshot.

Controls and Boundaries

Layer	UCPE role	Not guaranteed
Position	Conditions translation and rotation	Perfect scene geometry
Intrinsics	Represents focal behavior and field of view	Physical lens metadata
Distortion	Supports non-pinhole behavior	Artifact-free extreme fisheye motion
Orientation	Controls initial pitch and roll	Stable horizon under every prompt
Content	Uses the base generator and text	Identity memory across renders

Hardware and Installation

Install UCPE on a CUDA-capable Linux workstation or compatible Linux environment. The authors document training on eight NVIDIA A800 GPUs for about one day, but that is a training statement, not an inference minimum. The quick demo is the correct first target for a local creator.

Prepare the Environment

git clone https://github.com/chengzhag/UCPE.git
cd UCPE
conda create -n UCPE python=3.11 -y
conda activate UCPE
conda install -c conda-forge "ffmpeg<8" libiconv libgl -y
pip install -r requirements.txt
pip install --no-build-isolation --no-cache-dir flash-attn==2.8.0.post2
pip install -e .
cd thirdparty/equilib && pip install -e .

Use versions in the current repository. FlashAttention is sensitive to CUDA, PyTorch, compiler, and GPU combinations. If the pinned package cannot build, confirm those components before changing unrelated dependencies. Optional GeoCalib, UniK3D, Q-Align, and ViPE environments support evaluation or data processing; they are not required for the basic demo.

Weights and First Run

The official README links the authors’ fine-tuned weights through OneDrive and instructs users to place them under logs/. Preserve the extracted checkpoint structure because the demo configuration expects it. Return to the repository root and run bash scripts/demo.sh. The documented output is logs/6wodf04s/demo. Treat that run ID as an example and compare it with the downloaded directory. Do not substitute an unofficial checkpoint without recording its base model, format, license, and camera encoding.

VRAM Planning

No official minimum-VRAM figure is stated. Record peak allocated and reserved memory during one supplied sample. If it fails, reduce resolution, frames, batch size, or evaluation extras only where the configuration supports those changes. CPU offload or quantization may help in forks, but neither is an official UCPE promise unless the relevant source documents it.

Reference and Prompt Context

A camera-controlled render needs two coordinated plans: what the world contains and how the virtual camera observes it. A text prompt describes subject, action, environment, and light. Camera parameters describe pose, motion, intrinsics, distortion, pitch, and roll. A reference frame, when the selected base pipeline accepts one, anchors appearance and opening composition; it does not replace trajectory data.

Camera Checklist

• Draw the subject, camera start, camera end, and travel direction.
• Choose one dominant move: dolly, orbit, crane, pan, tilt, or roll.
• State focal-length behavior separately from camera distance.
• Use distortion intentionally; extreme wide angles magnify geometry errors.
• Set pitch and roll explicitly when the horizon matters.
• Keep speed and acceleration gentle for the first test.
• Match the reference perspective to the initial camera.

Prompt Template

[shot size] of [fixed subject] in [environment].
Action: [one clear action].
Camera: [start] to [end], [speed and easing].
Lens: [focal behavior], [distortion], [depth of field].
Orientation: [pitch], [roll].
Motion look: [frame rate], [shutter-angle look].
Lighting: [key], [fill], [practicals].
Preserve [wardrobe, prop, layout, landmarks].

Example: Wide shot of the same architect in a charcoal coat beside a concrete model in a daylight studio. Slow clockwise orbit from front-left to profile, gentle ease-in and ease-out. Natural 35 mm look, low distortion, level pitch and zero roll, 24 fps with a 180-degree shutter look. Soft window key from camera-right. Preserve face, coat, table, model, and window positions.

Character, Scene, and Audio Consistency

Explicit camera control solves a different problem from continuity. UCPE may steer viewpoint while the face, wardrobe, prop, or set drifts. Build a character sheet and scene bible, repeat the same descriptive nouns, keep clips short, and compare first, middle, and final frames with approved references.

Separate Consistency Layers

• Subject consistency: face, hair, proportions, and signature features.
• First-frame consistency: opening composition relative to a reference.
• Scene continuity: layout, lighting, props, and landmarks.
• Voice identity: the approved recording or synthesis voice.
• Speech synthesis: generation of words and vocal performance.
• Lip sync: alignment of mouth shapes with phonemes.
• Audio-driven motion: gestures and expression responding to sound.

UCPE addresses camera geometry, not the audio layers. Generate or record authorized speech separately, use a dedicated lip-sync or audio-driven system when needed, and preserve consent and provenance for synthetic voices.

Practical Workflow

Preview, Then Scale

1. Clone the current repository and note its commit.
2. Create the documented environment and test imports.
3. Download the official checkpoint and verify its directory tree.
4. Run scripts/demo.sh unchanged for a baseline.
5. Record output, runtime, peak VRAM, resolution, frames, and configuration.
6. Change only the prompt and inspect content versus camera behavior.
7. Change one camera parameter and compare trajectory and horizon.
8. Add a matched reference only if the base pipeline supports it.
9. Render a short continuity test before a production clip.
10. Export approved shots separately and assemble them in an editor.

Settings Checklist

Setting	First test	Scale after
Resolution	Demo default	Geometry passes
Frames	Shortest useful clip	Key frames pass
Motion	One gentle move	Horizon stays stable
Lens	Moderate field of view	Edges remain plausible
Prompt	One subject and action	Identity stays stable
Export	Native frames	Edit and deliver

Limitations and Troubleshooting

UCPE is research code with multiple datasets, third-party tools, and experiment configurations. The authors acknowledge jitter caused by inaccurate ViPE pose estimation in some synthesized examples. Camera adherence can also compete with fidelity when trajectories, distortion, subject motion, and prompt complexity are all extreme.

Common Fixes

Problem	Likely cause	Fix
FlashAttention fails	Stack mismatch	Match CUDA, PyTorch, compiler, and GPU
Checkpoint missing	Wrong run directory	Compare logs/ with configuration
Out of memory	Large render	Return to demo and measure each increase
Path jitters	Noisy poses or complex motion	Smooth path and test one move
Horizon drifts	Pitch or roll conflict	Set orientation and simplify trajectory
Character changes	No identity control	Use a shot reference and continuity bible
Edges bend	Aggressive distortion	Start moderate and increase gradually

Edit AI videos here

Assemble approved shots, trim unstable frames, add authorized voice, captions, music, transitions, and delivery formats at https://ai.alphatechnologies.vn. Editing controlled clips is more reliable than forcing one generation to perform every camera, scene, and audio change.

Final Recommendation

Start with the exact official demo, log the environment and checkpoint, and measure VRAM instead of trusting an unsupported number. Validate camera geometry with a simple scene before adding identity, props, speech, or complex motion. Explore Aikolhub for more production-tested open video AI workflows and keep a reproducible record of every accepted shot.

Frequently Asked Questions

Is UCPE open-source?

Yes. The official code uses MIT. Review separate checkpoint and base-model terms for your use.

How much VRAM does UCPE need?

The repository states no inference minimum. Run the demo, measure peak memory, and scale from that baseline.

Does UCPE support reference images?

UCPE is a camera-encoding method. Reference behavior depends on the integrated base pipeline and configuration.

Can UCPE control focal length and distortion?

It represents intrinsics and distortion plus 6-DoF pose. Inspect field of view, edges, faces, and horizon.

Does UCPE control voice or lip sync?

No. Voice identity, speech synthesis, lip sync, and audio-driven motion require separate workflows.

Official sources: UCPE repository, setup, updates, weights, and MIT license; UCPE technical paper; UCPE paper page.