'camera' 검색 결과

当你在稿件被《自动化学报》(Acta Automatica Sinica, AAS) 录用后、需要完成定稿、清样校对、补齐署名与基金、按本刊体例排版并处理稿酬与出版排期时调用。覆盖录用后从匿名稿恢复作者信息、套用本刊定稿模板、中英文摘要与参考文献终校、校样(清样)逐处核对、样刊与稿酬安排，帮助作者在 Acta Automatica Sinica 见刊前把清样打磨到零差错并理解稿酬与出版流程。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when planning the end-to-end ACM MM (ACM Multimedia) campaign calendar — from thematic-area scoping and track choice, through the April OpenReview abstract/paper/supplement chain, the June anonymous rebuttal, the July decision, the August camera-ready, and the November presentation in Rio, with the right skill invoked at each stage.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

当稿件被《电子学报》(Acta Electronica Sinica, AES) 录用后进入定稿、校样与清样阶段时使用。覆盖录用通知后的版权协议签署、按最终排版规范核对中英文题名/摘要/关键词/中图分类号、补全双盲阶段隐去的作者信息与基金编号、GB/T 7714 参考文献终校、图表与公式的印刷级质量、校样(清样)一校二校流程、版面费缴纳、DOI 与出版时序确认。帮助电子/信息/通信方向作者把已录用稿件零差错地送到《电子学报》正式出版。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when preparing an accepted ACM CCS paper for the ACM Digital Library proceedings, covering de-anonymization, the ACM sigconf final format, ACM rights and eRights forms, badge placement from artifact evaluation, incorporation of shepherd and minor-revision requirements, disclosure-timing coordination, registration, and in-person presentation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when planning an ACL project calendar across ACL Rolling Review cycles, covering cycle selection against conference commitment windows, backward planning from an ARR deadline, the review-response-meta-review timeline, commitment decisions, resubmission loops to later cycles, and camera-ready through ACL Anthology publication.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when planning an AAAI project timeline from topic selection through abstract, OpenReview submission, two-phase review, rebuttal, decision, camera-ready, registration, presentation, and public artifact release.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when planning an AAMAS project timeline from venue fit through abstract, full-paper or extended-abstract upload, supplementary material, the double-blind rebuttal, decision, IFAAMAS camera-ready, registration, and presentation, with track-specific deadlines and backward-planning offsets for a game-theory-plus-experiments paper.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generate an AI creator talking to camera, saying an approved script, as one continuous track — H3 Max reference-to-video through the GooseWorks fal-proxy (bills the Ads agent). Plans takes on line boundaries under H3's 15s cap, dry-runs a cost estimate, generates the first take alone so its voice can be locked and passed to every later take, joins takes with measured 0.10s dissolves, and moves each line's timing onto the words actually spoken. Use for any format with a generated creator speaking a script (split-screen, screen inserts, talking-head ads).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Render a 'hand-swipe flavor listicle' video from a config — the brand's REAL product cutouts each float on a flat flavor-matched color field under a persistent title and slide RIGHT-TO-LEFT one to the next like a phone-feed scroll, while a green-screen hand clip (back of hand toward camera, index finger up) is trimmed to its clean finger-up window, looped once per flavor, keyed with a green-dominance alpha key, scaled down and anchored at the bottom, with an optional end card and an optional instrumental music bed — deterministic PIL plus FFmpeg assembly, FREE (the hand clip comes from create-video-fal and the bed from create-music-elevenlabs), so labels stay pixel-crisp. Use for the hand-swipe-flavor-listicle format.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when working with camera, photos, audio, haptics, ShazamKit, on-device music analysis, face grouping and video highlights, screen recording, casting to non-AirPlay routes, SharePlay playback, the user's Apple Music library, lock-screen metadata, or CarPlay app design, templates, and navigation. Covers AVCaptureSession, AVFoundation, Core Haptics, MediaPlayer, MusicKit, NowPlaying, ScreenCaptureKit, DockKit.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Implement computer vision features including text recognition (OCR), face detection, barcode scanning, image segmentation, object tracking, and document scanning in iOS apps. Covers both the modern Swift-native Vision API (iOS 18+) and legacy VNRequest patterns, VisionKit DataScannerViewController for live camera scanning, and CoreMLRequest/VNCoreMLRequest for custom model inference. Use when adding OCR, barcode scanning, face detection, or custom Core ML model inference with Vision.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build iOS augmented reality and 3D experiences with RealityKit and ARKit. Use when adding RealityView content, loading entities or USDZ models, anchoring objects to planes or world positions, distinguishing entity hit tests from ARKit real-world raycasts, handling AR camera availability, world tracking, scene updates, or RealityKit entity gestures and interactions.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Maintain and extend existing SceneKit 3D scenes and visualizations. Use when working with SCNView, SCNScene, SCNNode scene graphs, SceneKit geometry/materials/lights/cameras, SCNAction animation, SCNPhysicsBody physics, SCNParticleSystem effects, .scn/.dae/.abc SceneKit assets, shader modifiers, or SwiftUI SceneView. SceneKit is soft-deprecated and in maintenance mode; route new apps, significant updates, USD/USDZ pipelines, and migration planning toward RealityKit.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build 2D games and animations using SpriteKit. Use when creating game scenes with SKScene and SKView, adding sprites with SKSpriteNode, animating with SKAction sequences, simulating physics with SKPhysicsBody and contact detection, creating particle effects with SKEmitterNode, building tile maps, using SKCameraNode, or integrating SpriteKit scenes in SwiftUI with SpriteView.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Trigger when: (1) User mentions "manim" or "Manim Community" or "ManimCE", (2) Code contains `from manim import *`, (3) User runs `manim` CLI commands, (4) Working with Scene, MathTex, Create(), or ManimCE-specific classes. Best practices for Manim Community Edition - the community-maintained Python animation engine. Covers Scene structure, animations, LaTeX/MathTex, 3D with ThreeDScene, camera control, styling, and CLI usage. NOT for ManimGL/3b1b version (which uses `manimlib` imports and `manimgl` CLI).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Trigger when: (1) User mentions "manimgl" or "ManimGL" or "3b1b manim", (2) Code contains `from manimlib import *`, (3) User runs `manimgl` CLI commands, (4) Working with InteractiveScene, self.frame, self.embed(), ShowCreation(), or ManimGL-specific patterns. Best practices for ManimGL (Grant Sanderson's 3Blue1Brown version) - OpenGL-based animation engine with interactive development. Covers InteractiveScene, Tex with t2c, camera frame control, interactive mode (-se flag), 3D rendering, and checkpoint_paste() workflow. NOT for Manim Community Edition (which uses `manim` imports and `manim` CLI).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Import raw data (CSV, XLSX, TXT, or MATLAB tables) into formats used by Sensor Fusion and Tracking Toolbox. Handles both ground truth trajectories and sensor detection data. For truth: builds trackingScenarioRecording, tuning timetable, truthlog, or converted table. For sensor data: builds task-oriented dataFormat structs (preferred) or objectDetection arrays (legacy). Use when importing flight logs, GPS logs, radar detections, IR measurements, lidar/camera bounding boxes, ADS-B data, AIS ship tracks, or any recorded data for use with trackers, filter tuning, or tracker evaluation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generate driving scenes, scenarios, road surfaces, and 3D content from scenariobuilder.* sensor data (GPS, camera, lidar, actor tracks) using Scenario Builder for Automated Driving Toolbox. BUILD, EXPORT, or AUGMENT a virtual scenario/scene/map: ego or actor trajectories, trajectory smoothing, OpenCRG road-surface extraction, 3D asset generation, static-object placement, point-cloud georeferencing + elevation, lane-based ego localization, sensor-fusion tracking, scenario-event extraction (cut-ins, hard brakes, near-misses, ADAS disengagements), or export to RoadRunner, drivingScenario, OpenDRIVE, OpenCRG, OpenSCENARIO, or Unreal Engine. Also: log-to-scenario, scenario harvesting, accident/near-miss reconstruction, SOTIF (ISO 21448) and ISO 26262 scenario coverage, USGS-aerial-lidar augmentation, traffic-sign placement, vision-based vehicle classification for actor assets. NOT for raw-data import or multi-sensor sync/crop/offset/timestamp normalization — route those to matlab-import-driving-data.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Comprehensive skill for Three.js 3D web development. Use this skill when building interactive 3D scenes, WebGL/WebGPU applications, product configurators, 3D visualizations, or immersive web experiences. Triggers on tasks involving Three.js, 3D rendering, scenes, cameras, meshes, materials, lights, animations, textures, or WebGL/WebGPU rendering.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when recreating a trending reel, TikTok or Short with a different performer: the same dance, moves, timing and camera, performed by the user's own character from a reference sheet, optionally in another outfit or a redecorated room, keeping the original song in sync. Keywords: reel recast, trend remake, TikTok dance, replace the dancer, character swap, choreography transfer, video to video, keep the music, Seedance edit mode.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when a 2D side-scroller shot, platformer cutscene, game trailer or parallax animation has to be built in Blender from Scenario-generated pictures (flat image cards, an orthographic camera, keyed jumps, rigid-body props), or when a finished shot needs a new theme or look without re-animating. Also use when image planes in Blender float above the ground, a tiled parallax layer shows a seam, or physics props leave the picture plane.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when an agent works in an open Blender scene through the Scenario for Blender extension's local MCP server (scenario-blender on 127.0.0.1:9876): connecting Claude Code, Cursor, Claude Desktop or Codex to Blender, the headless scenario_blender command, quoting and generating Scenario images, PBR materials, 3D meshes, video, audio or 360 environments from the scene, capturing the viewport or selected meshes as references, placing saved results, Render Video camera paths, or execute_python.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when building a 2D game in Godot 4.7: make a platformer or top-down controller that feels right (coyote time, jump buffer, jump height), TileMapLayer and TileSet in code, terrains and autotiles that pick wrong tiles, a dual grid, a camera that jitters or stutters, pixel art that looks blurry or uneven, stretch and integer scaling, 2D lights and shadows, y-sort order wrong, parallax gaps, or importing AI-generated sprites and tilesets from Scenario.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when building 3D levels and worlds in Godot 4.7: third-person or FPS controller, player stuck on stairs, spring arm camera clipping, blockout with CSG, GridMap and MeshLibrary, terrain (heightmap, HeightMapShape3D, Terrain3D regions, textures, painting), scattering trees with MultiMesh, streaming a big world, procedural meshes with SurfaceTool, Jolt physics settings, importing GLB or glTF from Blender or AI generators (Scenario, Tripo, Meshy, Hitem3D), import scripts, name suffixes, model facing, collision generation, LODs.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.