'camera' 검색 결과

Use when the user asks about camera movements, shot types, or how to describe camera behavior in a Higgsfield prompt. Contains all named camera controls with descriptions, best use cases, and example prompt phrases.

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Captions living in 3D space around a talking head: a camera flies between the speaker and the words (whip-in, parallax truck, push), caption groups sit at different depths so camera moves pull them apart, hero words hide BEHIND the speaker through an alpha matte, a ring of words wraps round the speaker and turns in front of them, focus racks between depths, and text steps at 15 fps with ghost motion blur. Works in any type style (editorial serif, bold sans) or hand-drawn (p5.brush write-on). Trigger on: "3D text", "3D camera captions", "text around / behind the speaker", "camera moves through the text", "depth captions on my avatar video". Covers prep (portion, padded plate, matte, word clock, font metrics), the shot grammar, depth rules, QC, and limits.

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Look at real hardware, screens, panels, and wiring on a desk or workbench through a camera, so Claude can SEE physical output that no unit test, log, or API can. Use whenever the user says "are you seeing this?", "olha isso", "look at this", "watch this", "can you see...", "check the camera", "look at the screen" / "look at the display", "is this wired right?", or "what's this part?"; asks to visually verify a rendered display, LCD, LED, or panel; wants a wiring, polarity, or voltage-rail check before power-on; or needs to confirm a font, layout, clipping, or on-screen value renders correctly instead of trusting green tests. Grabs the current camera frame(s) and reads them; supports a watch mode for an action in progress. Needs a snapshot-capable camera reachable over HTTP (Android IP Webcam app, Raspberry Pi camera-streamer, or any snapshot URL) set via CCE_CAM_URL.

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Train and evaluate models that estimate human poses from WiFi signals. Compare results against a model that always predicts the average pose, check for data leakage, and report measured improvements with clear evaluation conditions.

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• Train a pose model using camera labels
• Evaluate pose estimation without a camera at inference
• Compare accuracy against an average-pose baseline

Train and evaluate RuView models using Wi-Fi measurements for pose estimation, retrieval, and adaptation to different environments. Follow laptop or GCloud GPU workflows, then package and publish trained models to Hugging Face.

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• Train pose estimation without cameras or labels
• Train pose estimation with paired camera and Wi-Fi data
• Adapt models to different rooms and environments

Control CARLA’s on-screen camera to view a vehicle from behind, above, the driver’s seat, or the front. Select vehicles by name, role, or ID, frame them once, or follow them as they move.

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• Following the ego vehicle from behind
• Viewing a Toyota Prius from the driver’s seat
• Watching a vehicle from above

Places cameras, lidar, and other sensors in a running CARLA world, attached to vehicles or fixed in place. Configures mounting positions and sensor attributes, enables supported ROS 2 data publishing, and reports sensor IDs for later data reading.

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• Mounting a dashcam on the ego vehicle
• Preparing a lidar for point cloud capture
• Adding GNSS or collision sensors

Build a continuous 3D website where scrolling guides the camera, lighting, text, and interactions through planned chapters. The skill covers visual planning, responsive composition, progressive loading, accessibility, and performance checks for a live Three.js world.

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• Create a product story set in a continuous 3D world
• Build an interactive portfolio with a scrolling camera journey
• Present an exhibition through connected spatial chapters

Use when a Unity 6.3 task involves character animation or cameras: Mixamo or mocap humanoid import and retargeting, Animator Controllers, blend trees, layers and avatar masks, root motion, foot sliding, IK and Animation Rigging, Playables, Timeline cutscenes, custom tracks and markers, Cinemachine 3 cameras, camera jitter, flipping blends or cameras hitting walls; or when clips import with no animation, a character T-poses, an idle drifts, feet slide or the head misses its aim target.

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Use when turning a script or story into previs, layout, storyboards or an animatic in Blender: beats to shot list, camera and lens per shot, staging, camera height, 180-degree rule, screen direction, shot sizes, when to cut, camera moves, timing stills in the Video Sequencer, markers bound to cameras, Story Tools, Grease Pencil or 2D/3D hybrid boards. Also when an animatic shows the wrong frame, blocking keys are not stepped, Rigify operators are missing, or viewport render fails headless.

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Comprehensive best practices for robot perception systems covering cameras, LiDARs, depth sensors, IMUs, and multi-sensor setups. Use this skill when working with RGB image processing, depth maps, point clouds, sensor calibration (intrinsic, extrinsic, hand-eye), object detection, semantic segmentation, 3D reconstruction, visual servoing, or perception pipeline optimization. Trigger whenever the user mentions OpenCV, Open3D, PCL, RealSense, ZED, OAK-D, camera calibration, AprilTags, ArUco markers, stereo vision, RGBD, point cloud filtering, ICP registration, coordinate transforms, camera intrinsics, distortion correction, image undistortion, sensor streaming, frame synchronization, or any computer vision task in a robotics context. Also covers multi-camera rigs, time synchronization across sensors, perception latency budgets, and production deployment of perception pipelines.

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Transforms police body camera transcripts into structured 10-section legal summaries with timestamp-cited verbatim statements, constitutional issue spotting, evidence inventory, and probable cause articulation. Maintains strict neutrality for all parties. Use when analyzing BWC footage for discovery, pre-trial prep, or trial. Trigger: body camera, BWC, body worn camera, officer footage, use of force review, incident summary.

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Find live webcams within a chosen map area around a port, airport, border crossing, city, or route. Retrieve a selected camera's thumbnail and player links, along with its update time, for visual context near a location.

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• Finding webcams near a port or airport
• Locating cameras around a border crossing
• Finding visual context along a route or in a city

Discover what a paired phone or wearable currently supports, then run the requested device, app, camera, media, or smart-home action. Check each device's published capabilities and permissions, and report the actual outcome or any execution errors.

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• Check which actions a paired device supports
• Run supported device controls or app actions
• Use available camera or media actions

Generate cinematic clips with Seedance 2.0 using camera directions, multiple shots, and synchronized speech, sound effects, and ambience. Reference images help maintain character appearance, while the skill guides prompt structure, provider selection, previews, and checks of the finished clips.

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• Producing trailers and teaser clips
• Generating multiple shots with a consistent character
• Creating dialogue scenes with matching mouth movements

Build editable Three.js worlds from text or structured briefs, with terrain, distinct regions, landmarks, vegetation, and camera paths. Review the environment from multiple viewpoints and refine selected areas while preserving the overall layout and reproducible settings.

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• Build a continuous 3D landscape from a written brief
• Plan a camera fly-through across distinct regions
• Refine one area's terrain or vegetation placement

Add mouse, touch, and keyboard interaction to Three.js scenes. Implement object picking, camera navigation, dragging, and transform handles, with examples for selection feedback, coordinate conversion, and limiting the cost of interaction checks.

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• Select 3D objects with clicks or touch
• Add camera rotation and zoom controls
• Drag, rotate, and scale objects

Create mathematical and geometric animations in Python with Manim Community Edition. Use guidance, examples, and scene templates to structure animations, display equations, plot graphs, control cameras, and render two-dimensional or three-dimensional scenes.

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• Animating equations and derivations
• Creating function graphs and area visualizations
• Building 3D surfaces with camera movement

Create mathematical animations in ManimGL using Python, with guidance for equations, graphs, 3D scenes, and camera movement. Includes interactive development workflows, examples, and templates for testing changes and rendering scenes to video.

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• Animate equations and mathematical derivations
• Visualize functions and graphs
• Build 3D scenes with camera movement

Create a 22.5-second ice animation in HyperFrames, with an orbiting camera following a logo that breaks apart and reforms into two text messages. Customize the SVG logo, wording, ice appearance, and shard movement.

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• Create an ice-themed logo animation
• Present two messages in one continuous sequence
• Adjust ice transparency, frost, and cracks

Prepare and launch Autoware with CARLA on UE 5.8 through its native ROS 2 interface. Check communication topics and sensors, generate required maps, and configure either the classical stack or camera-only VAD model.

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• Check whether a CARLA build supports Autoware
• Inspect topics exchanged between CARLA and Autoware
• Generate lanelet2 and point-cloud maps for a town

Create a WebGL background with a perspective grid, fading lines, and sparse floating particles. Add slow forward motion and gentle mouse-responsive camera movement, then tune density, color, and depth to support foreground content.

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• Create a tech-style background with depth
• Add gentle mouse-responsive camera movement
• Tune fading grid lines and sparse particles

Build sky backgrounds for 3D scenes using procedural atmosphere or panoramic images. Align the horizon, sun, shadows, and environment lighting, while handling camera movement, image loading, and smooth transitions between day, dusk, and night.

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• Creating sky backgrounds for architecture and outdoor scenes
• Aligning sunset skies with shadows and reflections
• Designing transitions from day through dusk to night

Build falling leaves in Three.js and similar 3D scenes, with visible front, edge, and back views as they tumble and drift. Coordinate wind, scene depth, camera-aware recycling, and performance checks for a convincing autumn effect.

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• Adding falling autumn leaves to a 3D scene
• Coordinating leaf tumble, sideways drift, and wind
• Managing leaves around buildings and camera movement