{"id":10197,"date":"2026-07-14T14:48:35","date_gmt":"2026-07-14T06:48:35","guid":{"rendered":"https:\/\/infernews.com\/blog\/motion4motion\/"},"modified":"2026-07-14T14:49:29","modified_gmt":"2026-07-14T06:49:29","slug":"motion4motion","status":"publish","type":"post","link":"https:\/\/infernews.com\/blog\/motion4motion\/","title":{"rendered":"Motion4Motion\uff1a\u514d\u8a13\u7df4\u8de8\u7269\u7a2e\u52d5\u4f5c\u8f49\u79fb"},"content":{"rendered":"\n
\"Og<\/figure>\n\n\n\n

\u7576\u89d2\u8272\u5916\u5f62\u5dee\u5f88\u9060\uff0c\u50b3\u7d71\u52d5\u4f5c\u8f49\u79fb\u6700\u6613\u5361\u5728\u9aa8\u67b6\u5c0d\u5514\u4e0a\uff1a\u4eba\u3001\u9d5d\u3001\u718a\u8c93\u6839\u672c\u5187\u5171\u540c\u9aa8\u9abc\u6a21\u677f\uff0c\u7d50\u679c\u5e38\u898b\u554f\u984c\u5305\u62ec\u52d5\u4f5c\u50f5\u786c\u3001\u8eab\u4efd\u6f02\u79fb\uff0c\u6216\u8005\u8173\u6b65\u6ed1\u52d5\u3002Motion4Motion \u5c6c\u65bc\u5f71\u50cf\u751f\u6210\uff0f\u8996\u983b\u6a21\u578b\u65b9\u5411\u7684\u7814\u7a76\uff0c\u76f4\u63a5\u907f\u958b\u9aa8\u67b6\u8868\u793a\uff0c\u6539\u70ba\u5f9e\u4f86\u6e90\u5f71\u7247\u62bd\u53d6\u5bc6\u96c6 motion flow\uff0c\u518d\u628a\u52d5\u4f5c\u5e36\u5230\u53e6\u4e00\u500b\u76ee\u6a19\u4e3b\u9ad4\u8eab\u4e0a\u3002<\/p>\n\n\n\n

\u5462\u500b\u9805\u76ee\u7684\u53ef\u53d6\u4e4b\u8655\uff0c\u5728\u65bc training-free<\/strong>\u3002\u5b83\u5514\u9700\u8981\u70ba\u8de8\u7269\u7a2e\u6216\u8de8\u62d3\u64b2\u89d2\u8272\u53e6\u884c\u8a13\u7df4\u6a21\u578b\uff0c\u800c\u4fc2\u5efa\u57fa\u65bc\u51cd\u7d50\u7684 Diffusion Transformer\uff08WAN-T2V<\/a>\uff09\u505a\u5169\u968e\u6bb5\u63a8\u7406\uff1a\u5148\u627e\u51fa\u4f86\u6e90\u5f71\u7247\u4e3b\u9ad4\u7684\u904b\u52d5\u8ecc\u8de1\uff0c\u518d\u7528 TransPE\uff08Transferring Positional Encoding\uff09\u628a\u91cd\u5b9a\u5411\u5f8c\u7684\u4f4d\u7f6e\u4fe1\u606f\u6ce8\u5165\u6ce8\u610f\u529b\u904e\u7a0b\uff0c\u4ee4\u76ee\u6a19\u5916\u89c0\u8ddf\u4f4f\u52d5\u8d77\u4f86\u3002<\/p>\n\n\n\n

\u91cd\u9ede\u53ef\u4ee5\u6574\u7406\u6210\u5e7e\u9805\uff1a
\n– \u6bcb\u9808 skeleton\uff0c\u8f03\u6613\u8655\u7406 human \u2192 panda\u3001human \u2192 goose \u5462\u985e\u5916\u5f62\u5dee\u7570\u5927\u5605\u8f49\u79fb
\n– \u6bcb\u9808\u984d\u5916\u8a13\u7df4\uff0c\u63a8\u7406\u6642\u5b8c\u6210\u52d5\u4f5c\u9077\u79fb\uff0c\u90e8\u7f72\u9580\u6abb\u8f03\u4f4e
\n– \u7528 pixel-level motion flow \u53d6\u4ee3\u9aa8\u67b6\u9a45\u52d5\uff0c\u6e1b\u5c11\u9aa8\u67b6\u5c0d\u9f4a\u5931\u6548\u5e36\u4f86\u7684\u9650\u5236
\n– \u5efa\u57fa\u65bc\u9810\u8a13\u7df4\u8996\u983b diffusion transformer\uff0c\u4fdd\u7559\u751f\u6210\u5f0f\u6a21\u578b\u5c0d\u5916\u89c0\u7d30\u7bc0\u7684\u8868\u9054\u80fd\u529b<\/p>\n\n\n\n

\u5b83\u6703\u5148\u7528 Grounded SAM-2 \u5728\u4f86\u6e90\u9996\u5e40\u53d6\u6a23\u4e3b\u9ad4\u9328\u9ede\uff0c\u518d\u9760 diffusion features \u5efa\u7acb\u4f86\u6e90\u8207\u76ee\u6a19\u4e4b\u9593\u7684\u5c0d\u61c9\uff0c\u4e26\u4ee5 point tracker \u8ffd\u8e64\u6642\u9593\u4e0a\u7684\u8ecc\u8de1\u3002\u4e4b\u5f8c\u5728\u53bb\u566a\u968e\u6bb5\u91cd\u7528\u76ee\u6a19\u4e3b\u9ad4\u7684 K\/V\uff0c\u914d\u5408\u4f86\u81ea\u91cd\u5b9a\u5411 motion flow \u7684 RoPE \u4f4d\u7f6e\u7de8\u78bc\uff0c\u4ee4 self-attention \u6309\u65b0\u52d5\u4f5c\u91cd\u7d44\u756b\u9762\u3002<\/p>\n\n\n<\/div>\n\n\n

\u73fe\u6709\u8cc7\u6599\u6307\u51fa\uff0cMotion4Motion \u5728\u591a\u7d44\u5be6\u9a57\u8207\u61c9\u7528\u5c55\u793a\u4e2d\u52dd\u904e\u5f37\u57fa\u7dda\uff0c\u4ea6\u793a\u7bc4\u5230\u8de8\u7269\u7a2e\u8f49\u79fb\uff0c\u751a\u81f3\u53ef\u51fa\u73fe\u300c\u6559\u67b1\u884c\u8def\u300d\u5462\u985e\u975e\u5178\u578b\u6848\u4f8b\u3002\u9069\u5408\u7559\u610f\u89d2\u8272\u52d5\u756b\u3001\u8996\u983b\u751f\u6210\u3001\u52d5\u4f5c\u91cd\u5b9a\u5411\u5de5\u4f5c\u6d41\u5605\u8b80\u8005\uff1b\u4e0d\u904e\u76ee\u524d\u7c21\u4ecb\u4e3b\u8981\u96c6\u4e2d\u5728\u7814\u7a76\u5c55\u793a\uff0c\u5177\u9ad4\u7a69\u5b9a\u6027\u8207\u908a\u754c\u60c5\u6cc1\u4ecd\u8981\u7b49\u66f4\u5b8c\u6574\u5be6\u9a57\u7d30\u7bc0\u652f\u6301\u3002<\/p>\n\n\n\n

\u9805\u76ee\u4e3b\u9801<\/strong><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

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